Introduction

Loading Libraries and tyding the data

library(readr)
## Warning: package 'readr' was built under R version 3.5.3
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.5.3
## -- Attaching packages ----------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.1.1       v purrr   0.3.2  
## v tibble  2.1.1       v dplyr   0.8.0.1
## v tidyr   0.8.3       v stringr 1.4.0  
## v ggplot2 3.1.1       v forcats 0.4.0
## Warning: package 'ggplot2' was built under R version 3.5.3
## Warning: package 'tibble' was built under R version 3.5.3
## Warning: package 'tidyr' was built under R version 3.5.3
## Warning: package 'purrr' was built under R version 3.5.3
## Warning: package 'dplyr' was built under R version 3.5.3
## Warning: package 'stringr' was built under R version 3.5.3
## Warning: package 'forcats' was built under R version 3.5.3
## -- Conflicts -------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
setwd("D:/final")
Sal <- read_csv("Sal.csv")
## Parsed with column specification:
## cols(
##   year = col_double(),
##   geo_name = col_character(),
##   geo = col_character(),
##   soc_name = col_character(),
##   soc = col_character(),
##   sex_name = col_character(),
##   sex = col_double(),
##   num_ppl = col_double(),
##   num_ppl_moe = col_double(),
##   avg_wage_ft = col_double(),
##   avg_wage_ft_moe = col_double()
## )
race <- read_csv("race.csv")
## Parsed with column specification:
## cols(
##   year = col_double(),
##   geo_name = col_character(),
##   geo = col_character(),
##   soc_name = col_character(),
##   soc = col_character(),
##   race_name = col_character(),
##   race = col_double(),
##   num_ppl = col_double(),
##   num_ppl_moe = col_double(),
##   avg_wage = col_double(),
##   avg_wage_moe = col_double()
## )
employ1 <- read_csv("employ1.csv")
## Parsed with column specification:
## cols(
##   DATE = col_character(),
##   unemployment_rate = col_double()
## )
salfullrows <- read_csv("salfullrows.csv")
## Parsed with column specification:
## cols(
##   year = col_double(),
##   industry = col_character(),
##   agegrp = col_character(),
##   quarter = col_double(),
##   sex = col_character(),
##   EmpTotal = col_double(),
##   mon_wage = col_double(),
##   Payroll_sum = col_double()
## )
racefullrows <- read_csv("racefullrows.csv")
## Parsed with column specification:
## cols(
##   race = col_character(),
##   ethnicity = col_character(),
##   year = col_double(),
##   quarter1 = col_double(),
##   EmpTotal = col_double(),
##   EarnS = col_double(),
##   Payroll = col_double()
## )
predictedrace <- read_csv("predicted race.csv")
## Parsed with column specification:
## cols(
##   Year = col_double(),
##   Race = col_character(),
##   mean = col_double(),
##   sex = col_character(),
##   gender_mean = col_double(),
##   year1 = col_double(),
##   Unemploy = col_double(),
##   year2 = col_double()
## )
newemploy<- drop_na(employ1)
data(newemploy)
## Warning in data(newemploy): data set 'newemploy' not found
summary(newemploy)
##      DATE           unemployment_rate
##  Length:350         Min.   : 2.500   
##  Class :character   1st Qu.: 4.500   
##  Mode  :character   Median : 5.900   
##                     Mean   : 6.259   
##                     3rd Qu.: 7.875   
##                     Max.   :12.400
print(newemploy)
## # A tibble: 350 x 2
##    DATE      unemployment_rate
##    <chr>                 <dbl>
##  1 1/1/1990                6.6
##  2 2/1/1990                6.7
##  3 3/1/1990                7  
##  4 4/1/1990                6.9
##  5 5/1/1990                7  
##  6 6/1/1990                7.2
##  7 7/1/1990                7.7
##  8 8/1/1990                7.9
##  9 9/1/1990                8.2
## 10 10/1/1990               7.8
## # ... with 340 more rows

Timeseries

library(forecast)
## Warning: package 'forecast' was built under R version 3.5.3
library(readr)
library(dplyr)
newemploy$DATE <- as.Date(newemploy$DATE)
library(zoo)
## Warning: package 'zoo' was built under R version 3.5.3
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
tsdata <- ts(newemploy$unemployment_rate,start= 1990,end= 2018,frequency = 12)
# look at the trend
ddata <- decompose(tsdata)
plot(ddata)

plot(ddata$seasonal)

plot(ddata$trend)

class(tsdata)
## [1] "ts"
plot(newemploy$unemployment_rate,type="l",main="Time vs Unemployment rate")

#Arima model

mymodel <- auto.arima(tsdata)
mymodel
## Series: tsdata 
## ARIMA(1,1,1)(2,0,0)[12] 
## 
## Coefficients:
##          ar1      ma1    sar1    sar2
##       0.8435  -0.7164  0.3373  0.2951
## s.e.  0.0701   0.0881  0.0520  0.0548
## 
## sigma^2 estimated as 0.09186:  log likelihood=-76.4
## AIC=162.8   AICc=162.98   BIC=181.89

fitting the models

auto.arima(tsdata,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,1,2)(1,0,1)[12] with drift         : Inf
##  ARIMA(0,1,0)            with drift         : 275.9748
##  ARIMA(1,1,0)(1,0,0)[12] with drift         : 186.6235
##  ARIMA(0,1,1)(0,0,1)[12] with drift         : 225.276
##  ARIMA(0,1,0)                               : 274.1171
##  ARIMA(1,1,0)            with drift         : 277.5938
##  ARIMA(1,1,0)(2,0,0)[12] with drift         : 153.9113
##  ARIMA(1,1,0)(2,0,1)[12] with drift         : 149.3164
##  ARIMA(1,1,0)(1,0,1)[12] with drift         : Inf
##  ARIMA(1,1,0)(2,0,2)[12] with drift         : Inf
##  ARIMA(1,1,0)(1,0,2)[12] with drift         : Inf
##  ARIMA(0,1,0)(2,0,1)[12] with drift         : 160.4163
##  ARIMA(2,1,0)(2,0,1)[12] with drift         : 139.3738
##  ARIMA(2,1,0)(1,0,1)[12] with drift         : Inf
##  ARIMA(2,1,0)(2,0,0)[12] with drift         : 148.479
##  ARIMA(2,1,0)(2,0,2)[12] with drift         : Inf
##  ARIMA(2,1,0)(1,0,0)[12] with drift         : 185.3063
##  ARIMA(2,1,0)(1,0,2)[12] with drift         : Inf
##  ARIMA(3,1,0)(2,0,1)[12] with drift         : 135.6865
##  ARIMA(3,1,0)(1,0,1)[12] with drift         : Inf
##  ARIMA(3,1,0)(2,0,0)[12] with drift         : 140.3359
##  ARIMA(3,1,0)(2,0,2)[12] with drift         : Inf
##  ARIMA(3,1,0)(1,0,0)[12] with drift         : 179.438
##  ARIMA(3,1,0)(1,0,2)[12] with drift         : Inf
##  ARIMA(4,1,0)(2,0,1)[12] with drift         : 137.4434
##  ARIMA(3,1,1)(2,0,1)[12] with drift         : 137.0394
##  ARIMA(2,1,1)(2,0,1)[12] with drift         : 135.3993
##  ARIMA(2,1,1)(1,0,1)[12] with drift         : Inf
##  ARIMA(2,1,1)(2,0,0)[12] with drift         : 140.8232
##  ARIMA(2,1,1)(2,0,2)[12] with drift         : Inf
##  ARIMA(2,1,1)(1,0,0)[12] with drift         : 186.155
##  ARIMA(2,1,1)(1,0,2)[12] with drift         : Inf
##  ARIMA(1,1,1)(2,0,1)[12] with drift         : 132.8724
##  ARIMA(1,1,1)(1,0,1)[12] with drift         : Inf
##  ARIMA(1,1,1)(2,0,0)[12] with drift         : 141.5802
##  ARIMA(1,1,1)(2,0,2)[12] with drift         : Inf
##  ARIMA(1,1,1)(1,0,0)[12] with drift         : 184.3865
##  ARIMA(1,1,1)(1,0,2)[12] with drift         : Inf
##  ARIMA(0,1,1)(2,0,1)[12] with drift         : 153.907
##  ARIMA(1,1,2)(2,0,1)[12] with drift         : 133.3221
##  ARIMA(0,1,2)(2,0,1)[12] with drift         : 148.8814
##  ARIMA(2,1,2)(2,0,1)[12] with drift         : 137.0888
##  ARIMA(1,1,1)(2,0,1)[12]                    : 130.8857
##  ARIMA(1,1,1)(1,0,1)[12]                    : Inf
##  ARIMA(1,1,1)(2,0,0)[12]                    : 139.6814
##  ARIMA(1,1,1)(2,0,2)[12]                    : Inf
##  ARIMA(1,1,1)(1,0,0)[12]                    : 182.7696
##  ARIMA(1,1,1)(1,0,2)[12]                    : Inf
##  ARIMA(0,1,1)(2,0,1)[12]                    : 152.4477
##  ARIMA(1,1,0)(2,0,1)[12]                    : 147.6907
##  ARIMA(2,1,1)(2,0,1)[12]                    : 133.4222
##  ARIMA(1,1,2)(2,0,1)[12]                    : 131.3617
##  ARIMA(0,1,0)(2,0,1)[12]                    : 159.0808
##  ARIMA(0,1,2)(2,0,1)[12]                    : 147.383
##  ARIMA(2,1,0)(2,0,1)[12]                    : 137.5097
##  ARIMA(2,1,2)(2,0,1)[12]                    : 135.1501
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,1,1)(2,0,1)[12]                    : Inf
##  ARIMA(1,1,2)(2,0,1)[12]                    : Inf
##  ARIMA(1,1,1)(2,0,1)[12] with drift         : Inf
##  ARIMA(1,1,2)(2,0,1)[12] with drift         : Inf
##  ARIMA(2,1,1)(2,0,1)[12]                    : Inf
##  ARIMA(2,1,2)(2,0,1)[12]                    : Inf
##  ARIMA(2,1,1)(2,0,1)[12] with drift         : Inf
##  ARIMA(3,1,0)(2,0,1)[12] with drift         : Inf
##  ARIMA(3,1,1)(2,0,1)[12] with drift         : Inf
##  ARIMA(2,1,2)(2,0,1)[12] with drift         : Inf
##  ARIMA(4,1,0)(2,0,1)[12] with drift         : Inf
##  ARIMA(2,1,0)(2,0,1)[12]                    : Inf
##  ARIMA(2,1,0)(2,0,1)[12] with drift         : Inf
##  ARIMA(1,1,1)(2,0,0)[12]                    : 162.8024
## 
##  Best model: ARIMA(1,1,1)(2,0,0)[12]
## Series: tsdata 
## ARIMA(1,1,1)(2,0,0)[12] 
## 
## Coefficients:
##          ar1      ma1    sar1    sar2
##       0.8435  -0.7164  0.3373  0.2951
## s.e.  0.0701   0.0881  0.0520  0.0548
## 
## sigma^2 estimated as 0.09186:  log likelihood=-76.4
## AIC=162.8   AICc=162.98   BIC=181.89

residuals

library(tseries)
## Warning: package 'tseries' was built under R version 3.5.3
adf.test(mymodel$residuals)
## Warning in adf.test(mymodel$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel$residuals
## Dickey-Fuller = -6.2557, Lag order = 6, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel$residuals)

acf(ts(mymodel$residuals),main = "ACF Residual")

pacf(ts(mymodel$residuals),main = "PACF Residual")

#forecasting for unemployment

myforecast <- forecast(mymodel, level =c (95), h= 5*12)
plot(myforecast,main = "Unemployment forecasting for the next 5 years",xlab = "Time", ylab ="Unemployment rate")

myforecast[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          4.024446 4.165835 3.996864 3.900939 4.016970 4.041224
## 2019 3.706891 3.650299 3.726152 3.579489 3.546176 3.614015 3.621514
## 2020 3.358837 3.317245 3.384381 3.284899 3.245230 3.302249 3.311848
## 2021 3.124277 3.093520 3.138528 3.061672 3.038444 3.077683 3.083122
## 2022 2.942297 2.919645 2.954635 2.899351 2.879808 2.909868 2.914534
## 2023 2.811675                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 4.133759 4.092072 3.966653 3.715247 3.805754
## 2019 3.711173 3.637604 3.565381 3.391704 3.451452
## 2020 3.369323 3.332143 3.270717 3.137898 3.184723
## 2021 3.128958 3.094699 3.052659 2.956600 2.990021
## 2022 2.946955 2.924426 2.892117 2.820520 2.845611
## 2023

exponential smoothing forecast

eforecast <- ets(tsdata)
plot(forecast(eforecast,h=5*12))

AVerage for unemployment

Unemployment_rate <- 2.845611
cat("predicted average Unemployment_rate for the next 5 years in Miami Dade County in :",Unemployment_rate)
## predicted average Unemployment_rate for the next 5 years in Miami Dade County in : 2.845611

forecasting average salary male and females: dataset:1(monthly salary)

library(forecast)
library(readr)
newsalfullrows <- drop_na(salfullrows)
newsalfullrows$mon_wage <- as.numeric(newsalfullrows$mon_wage)
library(zoo)
newsalfullrows$year <- paste (newsalfullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
ns <- ts(newsalfullrows$mon_wage,start= 1997, end= 2018,frequency = 12)
ns1 <- decompose(ns)
plot(ns1)

plot(ns1$seasonal)

plot(ns1$trend)

plot(newsalfullrows$mon_wage,type ="l",main = "monthly salary of both genders",xlab= "Year",ylab="monthly Salary")

class(ns1)
## [1] "decomposed.ts"
ammodel <- auto.arima(ns)
ammodel
## Series: ns 
## ARIMA(2,0,0)(2,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ar2     sar1    sar2       mean
##       1.3446  -0.6045  -0.1348  0.4421  1972.9337
## s.e.  0.0516   0.0533   0.0593  0.0597   156.2309
## 
## sigma^2 estimated as 224906:  log likelihood=-1919.4
## AIC=3850.8   AICc=3851.14   BIC=3872
auto.arima(ns,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)            with non-zero mean : 4292.38
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3968.832
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 4056.747
##  ARIMA(0,0,0)            with zero mean     : 4643.492
##  ARIMA(1,0,0)            with non-zero mean : 4075.812
##  ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : 3948.25
##  ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)(2,0,0)[12] with non-zero mean : 4236.349
##  ARIMA(2,0,0)(2,0,0)[12] with non-zero mean : 3858.404
##  ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 3894.56
##  ARIMA(2,0,0)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(3,0,0)(2,0,0)[12] with non-zero mean : 3859.462
##  ARIMA(2,0,1)(2,0,0)[12] with non-zero mean : 3859.049
##  ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 3884.906
##  ARIMA(3,0,1)(2,0,0)[12] with non-zero mean : 3860.717
##  ARIMA(2,0,0)(2,0,0)[12] with zero mean     : 3888.17
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(2,0,0)(2,0,0)[12] with non-zero mean : 3850.803
## 
##  Best model: ARIMA(2,0,0)(2,0,0)[12] with non-zero mean
## Series: ns 
## ARIMA(2,0,0)(2,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ar2     sar1    sar2       mean
##       1.3446  -0.6045  -0.1348  0.4421  1972.9337
## s.e.  0.0516   0.0533   0.0593  0.0597   156.2309
## 
## sigma^2 estimated as 224906:  log likelihood=-1919.4
## AIC=3850.8   AICc=3851.14   BIC=3872
pred_sal <- forecast(ammodel, level =c (95), h= 5*12)
plot(pred_sal,main = "Monthly Salary forecasting for the next 5years",xlab = "Time", ylab ="monthly salary")

pred_sal[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          2101.752 1981.701 1543.999 1224.128 1331.827 1470.799
## 2019 1233.581 1369.261 1666.505 2095.019 2413.056 2704.334 2692.255
## 2020 2116.617 2110.595 2017.621 1766.577 1582.496 1590.981 1654.111
## 2021 1626.660 1687.457 1831.413 2054.717 2220.149 2347.786 2333.938
## 2022 2083.140 2072.281 2011.769 1870.677 1766.991 1753.534 1783.311
## 2023 1804.988                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 1786.758 2110.940 2052.702 1789.938 1498.338
## 2019 2207.144 1231.596 1362.172 1787.897 2023.740
## 2020 1859.199 2134.005 2090.601 1916.989 1756.245
## 2021 2091.820 1623.478 1687.055 1898.672 2024.609
## 2022 1906.624 2091.258 2063.497 1958.213 1870.168
## 2023

Forecasting for male monthly salary

library(forecast)
library(readr)
newsalfullrows <- drop_na(salfullrows)
newsalfullrows$mon_wage <- as.numeric(newsalfullrows$mon_wage)
malesalfullrows<- subset(newsalfullrows,sex == "Male",select=c(year,mon_wage))
library(zoo)
malesalfullrows$year <- paste (malesalfullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
malens <- ts(malesalfullrows$mon_wage,start= 1997, end= 2018,frequency = 12)
ns2 <- decompose(malens)
plot(ns2)

plot(ns2$seasonal)

plot(ns2$trend)

plot(malesalfullrows$mon_wage,type ="l",main = "male monthly salary ",xlab= "Year",ylab="monthly Salary")

class(ns2)
## [1] "decomposed.ts"
ammodel2 <- auto.arima(malens)
ammodel2
## Series: malens 
## ARIMA(3,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      ar2      ar3       mean
##       1.1979  -0.6064  -0.1148  2329.3796
## s.e.  0.0630   0.0908   0.0633    69.3817
## 
## sigma^2 estimated as 336984:  log likelihood=-1968.29
## AIC=3946.57   AICc=3946.81   BIC=3964.24
auto.arima(malens,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)            with non-zero mean : 4337.875
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 4026.044
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 4110.027
##  ARIMA(0,0,0)            with zero mean     : 4707.917
##  ARIMA(1,0,0)            with non-zero mean : 4147.831
##  ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 4102.343
##  ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 4222.714
##  ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 3951.643
##  ARIMA(2,0,0)            with non-zero mean : 3945.51
##  ARIMA(2,0,0)(0,0,1)[12] with non-zero mean : 3946.445
##  ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(3,0,0)            with non-zero mean : 3944.358
##  ARIMA(3,0,0)(1,0,0)[12] with non-zero mean : 3951.799
##  ARIMA(3,0,0)(0,0,1)[12] with non-zero mean : 3945.775
##  ARIMA(3,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(4,0,0)            with non-zero mean : 3946.079
##  ARIMA(3,0,1)            with non-zero mean : 3946.146
##  ARIMA(2,0,1)            with non-zero mean : 3944.988
##  ARIMA(4,0,1)            with non-zero mean : 3949.045
##  ARIMA(3,0,0)            with zero mean     : 4050.776
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(3,0,0)            with non-zero mean : 3946.571
## 
##  Best model: ARIMA(3,0,0)            with non-zero mean
## Series: malens 
## ARIMA(3,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      ar2      ar3       mean
##       1.1979  -0.6064  -0.1148  2329.3796
## s.e.  0.0630   0.0908   0.0633    69.3817
## 
## sigma^2 estimated as 336984:  log likelihood=-1968.29
## AIC=3946.57   AICc=3946.81   BIC=3964.24
pred_sal2 <- forecast(ammodel2, level =c (95), h= 5*12)
plot(pred_sal2,main = "male Monthly Salary forecasting for the next 5years",xlab = "Time", ylab ="monthly salary")

pred_sal2[["mean"]]
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2018           5231.4202 3864.8002 2067.0610  750.9499  421.4064 1031.0966
## 2019 2050.1832 1654.9187 1647.6697 1953.8035 2370.2875 2684.3847 2772.9441
## 2020 2234.4692 2387.3447 2482.9858 2489.1285 2420.9417 2324.5576 2249.7428
## 2021 2384.1863 2353.2004 2317.5399 2294.4607 2291.9957 2307.1315 2329.4065
## 2022 2315.0982 2316.4452 2323.4399 2331.7472 2337.3024 2338.1164 2334.7692
## 2023 2331.0619                                                            
##            Aug       Sep       Oct       Nov       Dec
## 2018 2112.3515 3075.6943 3504.0207 3308.8248 2704.6827
## 2019 2640.7530 2392.6447 2165.4318 2058.8812 2097.5065
## 2020 2226.3968 2254.8622 2311.7057 2365.2166 2391.5799
## 2021 2347.1944 2353.2574 2347.1767 2334.1743 2321.5901
## 2022 2329.6284 2325.4065 2323.8508 2325.1374 2328.1066
## 2023

forecasting for female monthly salary

library(forecast)
library(readr)
newsalfullrows <- drop_na(salfullrows)
newsalfullrows$mon_wage <- as.numeric(newsalfullrows$mon_wage)
femalesalfullrows<- subset(newsalfullrows,sex == "Female",select=c(year,mon_wage))
library(zoo)
femalesalfullrows$year <- paste (femalesalfullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
femalens <- ts(femalesalfullrows$mon_wage,start= 1997, end= 2018,frequency = 12)
ns3 <- decompose(femalens)
plot(ns3)

plot(ns3$seasonal)

plot(ns3$trend)

plot(femalesalfullrows$mon_wage,type ="l",main = " female monthly salary",xlab= "Year",ylab="monthly Salary")

class(ns3)
## [1] "decomposed.ts"
ammodel3 <- auto.arima(femalens)
ammodel3
## Series: femalens 
## ARIMA(5,0,0)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ar2      ar3      ar4     ar5    sar1       mean
##       1.1343  -0.2729  -0.0779  -0.5626  0.6089  0.4862  1636.0760
## s.e.  0.0499   0.0805   0.0821   0.0859  0.0554  0.0621   185.2207
## 
## sigma^2 estimated as 77840:  log likelihood=-1783.92
## AIC=3583.85   AICc=3584.44   BIC=3612.11
auto.arima(femalens,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)            with non-zero mean : 4035.823
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3704.982
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3814.333
##  ARIMA(0,0,0)            with zero mean     : 4499.861
##  ARIMA(1,0,0)            with non-zero mean : 3834.931
##  ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 3784.532
##  ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 3917.132
##  ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 3657.971
##  ARIMA(2,0,0)            with non-zero mean : 3649.704
##  ARIMA(2,0,0)(0,0,1)[12] with non-zero mean : 3651.181
##  ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(3,0,0)            with non-zero mean : 3648.346
##  ARIMA(3,0,0)(1,0,0)[12] with non-zero mean : 3655.537
##  ARIMA(3,0,0)(0,0,1)[12] with non-zero mean : 3649.868
##  ARIMA(3,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(4,0,0)            with non-zero mean : 3642.936
##  ARIMA(4,0,0)(1,0,0)[12] with non-zero mean : 3650.218
##  ARIMA(4,0,0)(0,0,1)[12] with non-zero mean : 3644.734
##  ARIMA(4,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(5,0,0)            with non-zero mean : 3620.184
##  ARIMA(5,0,0)(1,0,0)[12] with non-zero mean : 3585.583
##  ARIMA(5,0,0)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(5,0,0)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(5,0,0)(0,0,1)[12] with non-zero mean : 3609.814
##  ARIMA(5,0,0)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(5,0,1)(1,0,0)[12] with non-zero mean : 3587.316
##  ARIMA(4,0,1)(1,0,0)[12] with non-zero mean : 3629.315
##  ARIMA(5,0,0)(1,0,0)[12] with zero mean     : 3595.367
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(5,0,0)(1,0,0)[12] with non-zero mean : 3583.847
## 
##  Best model: ARIMA(5,0,0)(1,0,0)[12] with non-zero mean
## Series: femalens 
## ARIMA(5,0,0)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ar2      ar3      ar4     ar5    sar1       mean
##       1.1343  -0.2729  -0.0779  -0.5626  0.6089  0.4862  1636.0760
## s.e.  0.0499   0.0805   0.0821   0.0859  0.0554  0.0621   185.2207
## 
## sigma^2 estimated as 77840:  log likelihood=-1783.92
## AIC=3583.85   AICc=3584.44   BIC=3612.11
pred_sal3 <- forecast(ammodel3, level =c (95), h= 5*12)
plot(pred_sal3,main = "female Monthly Salary forecasting for the next 5years",xlab = "Time", ylab ="monthly salary")

pred_sal3[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          2832.026 2504.316 1939.245 1396.876 1538.079 1720.615
## 2019 1279.432 1290.414 1675.560 2179.379 2498.042 2577.023 2151.430
## 2020 2266.644 2311.405 2069.107 1675.903 1349.339 1336.993 1519.311
## 2021 1337.244 1299.165 1506.094 1824.608 2063.661 2119.790 1911.486
## 2022 1970.020 2018.125 1872.445 1619.784 1405.274 1364.491 1487.143
## 2023 1426.604                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 2027.967 2447.776 2648.321 2207.536 1672.656
## 2019 1562.783 1215.706 1278.317 1528.016 1948.803
## 2020 1823.455 2104.354 2188.744 1952.735 1607.044
## 2021 1585.567 1347.464 1323.235 1478.532 1749.507
## 2022 1706.333 1901.049 1957.836 1829.521 1611.062
## 2023

calculating monthly women salary for the year 2020

 womensal <- 2391.5799/1337.244
womensal
## [1] 1.788439
cat("The average mens monthly salary is ",womensal,"times of womens monthly salary")
## The average mens monthly salary is  1.788439 times of womens monthly salary

calculating how much women earns for every men who earns a dollar

wcents <- 1/womensal
wcents
## [1] 0.5591467
cat("The women earns",wcents*100, "cents for every men who earns a dollar")
## The women earns 55.91467 cents for every men who earns a dollar

forecasting for race 1: White Alone

library(forecast)
library(readr)
newracefullrows <- drop_na(racefullrows)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
wracefullrows<- subset(newracefullrows,race == "White Alone",select=c(year,EarnS))
wracefullrows$year <- paste (wracefullrows$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
wrc <- ts(wracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns4 <- decompose(wrc)
plot(ns4)

plot(ns4$seasonal)

plot(ns4$trend)

plot(wracefullrows$EarnS,type ="l",main = " whites monthly salary",xlab= "Year",ylab="monthly Salary")

class(ns4)
## [1] "decomposed.ts"
ammodel4 <- auto.arima(wrc)
ammodel4
## Series: wrc 
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean 
## 
## Coefficients:
##           ar1     ar2     ma1      ma2    sar1    sma1       mean
##       -0.0982  0.5446  0.9489  -0.0213  0.3511  0.2131  2586.3894
## s.e.   0.1121  0.0670  0.1288   0.1099  0.1527  0.1612   196.2576
## 
## sigma^2 estimated as 256665:  log likelihood=-1933.46
## AIC=3882.93   AICc=3883.52   BIC=3911.19
auto.arima(wrc,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3869.468
##  ARIMA(0,0,0)            with non-zero mean : 4070.132
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3883.139
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3930.748
##  ARIMA(0,0,0)            with zero mean     : 4717.555
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3891.533
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3872.735
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3871.372
##  ARIMA(2,0,2)            with non-zero mean : 3938.785
##  ARIMA(2,0,2)(0,0,2)[12] with non-zero mean : 3891.567
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3858.476
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(3,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,3)(2,0,2)[12] with non-zero mean : 3860.011
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(1,0,3)(2,0,2)[12] with non-zero mean : 3870.577
##  ARIMA(3,0,1)(2,0,2)[12] with non-zero mean : 3859.545
##  ARIMA(3,0,3)(2,0,2)[12] with non-zero mean : 3862.444
##  ARIMA(2,0,2)(2,0,2)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(3,0,1)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,3)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(3,0,3)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3882.928
## 
##  Best model: ARIMA(2,0,2)(1,0,1)[12] with non-zero mean
## Series: wrc 
## ARIMA(2,0,2)(1,0,1)[12] with non-zero mean 
## 
## Coefficients:
##           ar1     ar2     ma1      ma2    sar1    sma1       mean
##       -0.0982  0.5446  0.9489  -0.0213  0.3511  0.2131  2586.3894
## s.e.   0.1121  0.0670  0.1288   0.1099  0.1527  0.1612   196.2576
## 
## sigma^2 estimated as 256665:  log likelihood=-1933.46
## AIC=3882.93   AICc=3883.52   BIC=3911.19
pred_sal4 <- forecast(ammodel4, level =c (95), h= 3*12)
plot(pred_sal4,main = "whites Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")

pred_sal4[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          2196.714 2232.776 1945.297 2072.246 1955.623 2275.175
## 2019 2623.485 2435.071 2502.752 2349.444 2429.121 2356.200 2490.644
## 2020 2602.256 2531.519 2558.749 2502.087 2532.227 2504.866 2553.418
## 2021 2592.109                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 2237.881 2131.209 2765.251 2800.739 2735.714
## 2019 2457.949 2434.551 2645.079 2666.374 2636.112
## 2020 2540.848 2533.479 2606.708 2614.711 2603.666
## 2021

forecasting for race 2: Black or African American Alone

library(forecast)
library(readr)
newracefullrows <- drop_na(racefullrows)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
bracefullrows<- subset(newracefullrows,race == "Black or African American Alone",select=c(year,EarnS))
bracefullrows$year <- paste (bracefullrows$year,"/01/01",sep ="")
brc <- ts(bracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns5 <- decompose(brc)
plot(ns5)

plot(ns5$seasonal)

plot(ns5$trend)

plot(bracefullrows$EarnS,type ="l",main = " Blacks monthly salary",xlab= "Year",ylab="monthly Salary")

class(ns5)
## [1] "decomposed.ts"
ammodel5 <- auto.arima(brc)
ammodel5
## Series: brc 
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean 
## 
## Coefficients:
##          ar1     ma1    sar1     sar2     sma1      mean
##       0.6212  0.2779  0.8073  -0.3053  -0.6265  1921.688
## s.e.  0.0625  0.0743  0.1304   0.0631   0.1260    61.607
## 
## sigma^2 estimated as 137151:  log likelihood=-1853.95
## AIC=3721.91   AICc=3722.36   BIC=3746.64
auto.arima(brc,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3708.363
##  ARIMA(0,0,0)            with non-zero mean : 3941.113
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3722.455
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3786.034
##  ARIMA(0,0,0)            with zero mean     : 4568.404
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3730.33
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3710.085
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3707.466
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3710.932
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3706.48
##  ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3710.353
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3703.852
##  ARIMA(1,0,2)(1,0,2)[12] with non-zero mean : 3708.344
##  ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 3704.971
##  ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 3706.346
##  ARIMA(0,0,2)(2,0,2)[12] with non-zero mean : 3709.356
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3703.073
##  ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 3710.723
##  ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3704.449
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 3708.827
##  ARIMA(0,0,1)(2,0,2)[12] with non-zero mean : 3757.073
##  ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3714.132
##  ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : 3705.238
##  ARIMA(0,0,0)(2,0,2)[12] with non-zero mean : 3910.815
##  ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : 3703.261
##  ARIMA(1,0,1)(2,0,2)[12] with zero mean     : 3742.365
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3721.905
## 
##  Best model: ARIMA(1,0,1)(2,0,1)[12] with non-zero mean
## Series: brc 
## ARIMA(1,0,1)(2,0,1)[12] with non-zero mean 
## 
## Coefficients:
##          ar1     ma1    sar1     sar2     sma1      mean
##       0.6212  0.2779  0.8073  -0.3053  -0.6265  1921.688
## s.e.  0.0625  0.0743  0.1304   0.0631   0.1260    61.607
## 
## sigma^2 estimated as 137151:  log likelihood=-1853.95
## AIC=3721.91   AICc=3722.36   BIC=3746.64
pred_sal5 <- forecast(ammodel5, level =c (95), h= 3*12)
plot(pred_sal5,main = "blacks Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")

pred_sal5[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          1596.241 1686.469 1681.904 1704.883 1750.282 1716.900
## 2019 1861.512 1838.467 1926.831 2013.939 2018.004 2032.760 1914.195
## 2020 1939.506 1954.204 1997.864 2069.501 2065.717 2063.738 1978.192
## 2021 1954.446                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 1720.417 1831.786 1981.476 1983.431 1963.285
## 2019 1914.332 1984.590 1790.800 1790.900 1796.690
## 2020 1977.217 1999.929 1797.776 1797.257 1808.080
## 2021

forecasting for race 3: American Indian or Alaska Native Alone

library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
aracefullrows <- subset(newracefullrows,race == "American Indian or Alaska Native Alone",select=c(year,EarnS))
head(aracefullrows)
aracefullrows$year <- paste(aracefullrows$year,"/01/01",sep ="")
arc <- ts(aracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns6 <- decompose(arc)
plot(ns6)

plot(ns6$seasonal)

plot(ns6$trend)

plot(aracefullrows$EarnS,type ="l",main = "American Indian or Alaska Native Alone monthly salary",xlab= "Year",ylab="monthly Salary")

class(ns6)
## [1] "decomposed.ts"
ammodel6 <- auto.arima(arc)
ammodel6
## Series: arc 
## ARIMA(2,0,1)(2,0,1)[12] with non-zero mean 
## 
## Coefficients:
##           ar1     ar2     ma1    sar1     sar2     sma1       mean
##       -0.1147  0.5533  0.9410  0.7711  -0.1984  -0.3662  2106.3192
## s.e.   0.0717  0.0697  0.0394  0.3031   0.1273   0.2983   136.2093
## 
## sigma^2 estimated as 191849:  log likelihood=-1895.66
## AIC=3807.32   AICc=3807.91   BIC=3835.59
auto.arima(arc,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3791.532
##  ARIMA(0,0,0)            with non-zero mean : 3992.2
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3795.956
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3856.997
##  ARIMA(0,0,0)            with zero mean     : 4616.041
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3814.752
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3790.989
##  ARIMA(2,0,2)            with non-zero mean : 3838.94
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3787.969
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3786.205
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3783.49
##  ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3790.654
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3790.724
##  ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : 3781.735
##  ARIMA(2,0,1)(1,0,2)[12] with non-zero mean : 3788.696
##  ARIMA(2,0,1)(2,0,1)[12] with non-zero mean : 3784.409
##  ARIMA(2,0,1)(1,0,1)[12] with non-zero mean : 3789.534
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3788.727
##  ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : 3789.039
##  ARIMA(3,0,1)(2,0,2)[12] with non-zero mean : 3789.002
##  ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3788.239
##  ARIMA(3,0,0)(2,0,2)[12] with non-zero mean : 3790.836
##  ARIMA(3,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,1)(2,0,2)[12] with zero mean     : 3808.891
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(2,0,1)(2,0,1)[12] with non-zero mean : 3807.323
## 
##  Best model: ARIMA(2,0,1)(2,0,1)[12] with non-zero mean
## Series: arc 
## ARIMA(2,0,1)(2,0,1)[12] with non-zero mean 
## 
## Coefficients:
##           ar1     ar2     ma1    sar1     sar2     sma1       mean
##       -0.1147  0.5533  0.9410  0.7711  -0.1984  -0.3662  2106.3192
## s.e.   0.0717  0.0697  0.0394  0.3031   0.1273   0.2983   136.2093
## 
## sigma^2 estimated as 191849:  log likelihood=-1895.66
## AIC=3807.32   AICc=3807.91   BIC=3835.59
pred_sal6 <- forecast(ammodel6, level =c (95), h= 3*12)
plot(pred_sal6,main = "American Indian Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")

pred_sal6[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          1702.926 1882.618 1663.507 1735.157 1717.477 1892.051
## 2019 2020.267 1939.082 2042.279 1970.998 2002.158 2000.151 2049.716
## 2020 2088.162 2059.132 2100.194 2090.905 2098.877 2102.274 2104.682
## 2021 2109.250                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 1869.938 1880.354 2261.006 2293.533 2209.224
## 2019 2030.346 2059.284 2137.659 2148.381 2129.658
## 2020 2095.063 2114.549 2100.083 2101.409 2104.084
## 2021

forecasting for race 4: Asian Alone

library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
asracefullrows <- subset(newracefullrows,race == "Asian Alone",select=c(year,EarnS,race))
head(asracefullrows)
asracefullrows$year <- paste(asracefullrows$year,"/01/01",sep ="")
asrc <- ts(asracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns7 <- decompose(asrc)
plot(ns7)

plot(ns7$seasonal)

plot(ns7$trend)

plot(asracefullrows$EarnS,type ="l",main = "Asian Alone monthly salary",xlab= "Year",ylab="monthly Salary")

class(ns7)
## [1] "decomposed.ts"
ammodel7 <- auto.arima(asrc)
ammodel7
## Series: asrc 
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean 
## 
## Coefficients:
##          ar1     ma1    sar1     sar2     sma1    sma2       mean
##       0.5233  0.2778  0.8458  -0.6814  -0.4876  0.7440  2353.9891
## s.e.  0.0811  0.0937  0.2339   0.1356   0.2291  0.0859   139.7658
## 
## sigma^2 estimated as 319425:  log likelihood=-1964.64
## AIC=3945.29   AICc=3945.88   BIC=3973.56
auto.arima(asrc,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)            with non-zero mean : 4115.845
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3960.971
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3991.858
##  ARIMA(0,0,0)            with zero mean     : 4682.466
##  ARIMA(1,0,0)            with non-zero mean : 4010.36
##  ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : 3960.842
##  ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : 3959.003
##  ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : 3961.816
##  ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3953.41
##  ARIMA(1,0,0)(1,0,2)[12] with non-zero mean : 3957.728
##  ARIMA(0,0,0)(2,0,2)[12] with non-zero mean : 4087.26
##  ARIMA(2,0,0)(2,0,2)[12] with non-zero mean : 3950.507
##  ARIMA(2,0,0)(1,0,2)[12] with non-zero mean : 3951.366
##  ARIMA(2,0,0)(2,0,1)[12] with non-zero mean : 3955.018
##  ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : 3958.506
##  ARIMA(3,0,0)(2,0,2)[12] with non-zero mean : 3953.291
##  ARIMA(2,0,1)(2,0,2)[12] with non-zero mean : 3951.846
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3948.98
##  ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 3950.233
##  ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3954.113
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 3957.247
##  ARIMA(0,0,1)(2,0,2)[12] with non-zero mean : 3974.776
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3950.933
##  ARIMA(0,0,2)(2,0,2)[12] with non-zero mean : 3954.604
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3953.824
##  ARIMA(1,0,1)(2,0,2)[12] with zero mean     : 3986.575
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3945.288
## 
##  Best model: ARIMA(1,0,1)(2,0,2)[12] with non-zero mean
## Series: asrc 
## ARIMA(1,0,1)(2,0,2)[12] with non-zero mean 
## 
## Coefficients:
##          ar1     ma1    sar1     sar2     sma1    sma2       mean
##       0.5233  0.2778  0.8458  -0.6814  -0.4876  0.7440  2353.9891
## s.e.  0.0811  0.0937  0.2339   0.1356   0.2291  0.0859   139.7658
## 
## sigma^2 estimated as 319425:  log likelihood=-1964.64
## AIC=3945.29   AICc=3945.88   BIC=3973.56
pred_sal7 <- forecast(ammodel7, level =c (95), h= 3*12)
plot(pred_sal7,main = "Asian Alone Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")

pred_sal7[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          2246.788 1990.303 1688.875 1641.645 1499.334 2408.398
## 2019 1626.769 1575.951 1864.390 1913.247 1894.348 2002.206 2422.265
## 2020 2103.070 2050.241 2334.909 2511.466 2490.940 2659.929 2385.707
## 2021 2637.532                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 2409.603 2049.515 2349.585 2354.384 2123.790
## 2019 2415.149 2614.538 1867.633 1865.128 2162.898
## 2020 2373.603 2784.879 1947.189 1941.045 2349.654
## 2021

forecasting for race 5: Native Hawaiian or Other Pacific Islander Alone

library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
nracefullrows <- subset(newracefullrows,race == "Native Hawaiian or Other Pacific Islander Alone",select=c(year,EarnS,race))
head(nracefullrows)
nracefullrows$year <- paste(nracefullrows$year,"/01/01",sep ="")
nrc <- ts(nracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns8 <- decompose(nrc)
plot(ns8)

plot(ns8$seasonal)

plot(ns8$trend)

plot(nracefullrows$EarnS,type ="l",main = "Native Hawaiian or Other Pacific Islander Alone monthly salary",xlab= "Year",ylab="monthly Salary")

class(ns8)
## [1] "decomposed.ts"
ammodel8 <- auto.arima(nrc)
ammodel8
## Series: nrc 
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean 
## 
## Coefficients:
##           ar1      ar2     ma1     ma2     ma3    sar1    sar2       mean
##       -0.6083  -0.4481  1.4826  1.4596  0.7730  0.1992  0.1453  1925.2832
## s.e.   0.0775   0.0876  0.0562  0.0661  0.0488  0.0738  0.0715    79.4667
## 
## sigma^2 estimated as 144958:  log likelihood=-1860.42
## AIC=3738.83   AICc=3739.57   BIC=3770.63
auto.arima(nrc,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3742.029
##  ARIMA(0,0,0)            with non-zero mean : 3923.893
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3750.42
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3792.06
##  ARIMA(0,0,0)            with zero mean     : 4568.682
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3760.312
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3740.043
##  ARIMA(2,0,2)            with non-zero mean : 3773.233
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3741.812
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3743.027
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 3744.166
##  ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 3744.08
##  ARIMA(3,0,2)(1,0,0)[12] with non-zero mean : 3743.141
##  ARIMA(2,0,3)(1,0,0)[12] with non-zero mean : 3720.216
##  ARIMA(2,0,3)            with non-zero mean : 3757.906
##  ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 3717.277
##  ARIMA(2,0,3)(2,0,1)[12] with non-zero mean : 3718.794
##  ARIMA(2,0,3)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(1,0,3)(2,0,0)[12] with non-zero mean : 3722.853
##  ARIMA(3,0,3)(2,0,0)[12] with non-zero mean : 3733.996
##  ARIMA(2,0,4)(2,0,0)[12] with non-zero mean : 3726.845
##  ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 3745.564
##  ARIMA(1,0,4)(2,0,0)[12] with non-zero mean : 3723.353
##  ARIMA(3,0,2)(2,0,0)[12] with non-zero mean : 3740.366
##  ARIMA(3,0,4)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(2,0,3)(2,0,0)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 3738.831
## 
##  Best model: ARIMA(2,0,3)(2,0,0)[12] with non-zero mean
## Series: nrc 
## ARIMA(2,0,3)(2,0,0)[12] with non-zero mean 
## 
## Coefficients:
##           ar1      ar2     ma1     ma2     ma3    sar1    sar2       mean
##       -0.6083  -0.4481  1.4826  1.4596  0.7730  0.1992  0.1453  1925.2832
## s.e.   0.0775   0.0876  0.0562  0.0661  0.0488  0.0738  0.0715    79.4667
## 
## sigma^2 estimated as 144958:  log likelihood=-1860.42
## AIC=3738.83   AICc=3739.57   BIC=3770.63
pred_sal8 <- forecast(ammodel8, level =c (95), h= 3*12)
plot(pred_sal8,main = "Native Hawaiian or Other Pacific Islander Alone Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")

pred_sal8[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          1166.068 1936.361 1954.665 1755.968 2112.173 2125.093
## 2019 1660.343 1657.440 1883.304 1893.981 1830.489 2076.199 2087.439
## 2020 1739.209 1761.587 1918.528 1923.324 1881.794 1982.502 1986.621
## 2021 1849.717                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 2034.552 1828.888 1852.484 1771.206 1668.979
## 2019 2017.282 1870.838 1885.504 1834.802 1754.709
## 2020 1959.487 1900.429 1906.780 1884.868 1854.058
## 2021

forecasting for race 6: Two or More Race Groups

library(forecast)
library(readr)
newracefullrows$EarnS <- as.numeric(newracefullrows$EarnS)
tracefullrows <- subset(newracefullrows,race == "Two or More Race Groups",select=c(year,EarnS,race))
head(tracefullrows)
tracefullrows$year <- paste(tracefullrows$year,"/01/01",sep ="")
trc <- ts(tracefullrows$EarnS,start= 1997, end= 2018,frequency = 12)
ns9 <- decompose(trc)
plot(ns9)

plot(ns9$seasonal)

plot(ns9$trend)

plot(tracefullrows$EarnS,type ="l",main = "Two or More Race Groups",xlab= "Year",ylab="monthly Salary")

class(ns9)
## [1] "decomposed.ts"
ammodel9 <- auto.arima(trc)
ammodel9
## Series: trc 
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean 
## 
## Coefficients:
##          ar1     sar1   sma1       mean
##       0.6582  -0.2081  0.562  2121.6605
## s.e.  0.0474   0.1509  0.124   104.7002
## 
## sigma^2 estimated as 203352:  log likelihood=-1904.48
## AIC=3818.97   AICc=3819.21   BIC=3836.63
auto.arima(trc,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 3796.14
##  ARIMA(0,0,0)            with non-zero mean : 3973.154
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 3801.375
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 3856.026
##  ARIMA(0,0,0)            with zero mean     : 4617.079
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 3827.078
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 3805.261
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 3797.924
##  ARIMA(2,0,2)(1,0,2)[12] with non-zero mean : 3798.051
##  ARIMA(2,0,2)            with non-zero mean : Inf
##  ARIMA(2,0,2)(0,0,2)[12] with non-zero mean : 3828.083
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 3796.616
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 3799.845
##  ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 3793.098
##  ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 3824.137
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 3802.372
##  ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 3794.94
##  ARIMA(1,0,2)(1,0,2)[12] with non-zero mean : 3795
##  ARIMA(1,0,2)            with non-zero mean : 3844.543
##  ARIMA(1,0,2)(0,0,2)[12] with non-zero mean : 3825.075
##  ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 3793.623
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 3796.867
##  ARIMA(0,0,2)(1,0,1)[12] with non-zero mean : 3799.922
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 3791.436
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 3822.663
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 3801.19
##  ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 3793.187
##  ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 3793.319
##  ARIMA(1,0,1)            with non-zero mean : 3842.545
##  ARIMA(1,0,1)(0,0,2)[12] with non-zero mean : 3823.38
##  ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 3791.915
##  ARIMA(1,0,1)(2,0,2)[12] with non-zero mean : 3795.131
##  ARIMA(0,0,1)(1,0,1)[12] with non-zero mean : 3827.723
##  ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : 3791.282
##  ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 3822.746
##  ARIMA(1,0,0)(2,0,1)[12] with non-zero mean : 3792.581
##  ARIMA(1,0,0)(1,0,2)[12] with non-zero mean : 3793.18
##  ARIMA(1,0,0)            with non-zero mean : 3842.186
##  ARIMA(1,0,0)(0,0,2)[12] with non-zero mean : 3823.455
##  ARIMA(1,0,0)(2,0,0)[12] with non-zero mean : 3791.389
##  ARIMA(1,0,0)(2,0,2)[12] with non-zero mean : 3794.557
##  ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 3937.966
##  ARIMA(2,0,0)(1,0,1)[12] with non-zero mean : 3792.297
##  ARIMA(2,0,1)(1,0,1)[12] with non-zero mean : 3793.644
##  ARIMA(1,0,0)(1,0,1)[12] with zero mean     : 3829.254
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,0)(1,0,1)[12] with non-zero mean : 3818.966
## 
##  Best model: ARIMA(1,0,0)(1,0,1)[12] with non-zero mean
## Series: trc 
## ARIMA(1,0,0)(1,0,1)[12] with non-zero mean 
## 
## Coefficients:
##          ar1     sar1   sma1       mean
##       0.6582  -0.2081  0.562  2121.6605
## s.e.  0.0474   0.1509  0.124   104.7002
## 
## sigma^2 estimated as 203352:  log likelihood=-1904.48
## AIC=3818.97   AICc=3819.21   BIC=3836.63
pred_sal9 <- forecast(ammodel9, level =c (95), h= 3*12)
plot(pred_sal9,main = "Two or More Race Groups Monthly Salary forecasting for the next 3years",xlab = "Time", ylab ="monthly salary")

pred_sal9[["mean"]]
##           Jan      Feb      Mar      Apr      May      Jun      Jul
## 2018          1908.027 1905.768 1951.572 1939.979 1964.337 1988.491
## 2019 2170.082 2168.035 2167.846 2157.883 2160.009 2154.754 2149.605
## 2020 2111.606 2112.025 2112.060 2114.130 2113.686 2114.778 2115.848
## 2021 2123.753                                                      
##           Aug      Sep      Oct      Nov      Dec
## 2018 1997.149 1972.695 2342.373 2360.073 2269.647
## 2019 2147.722 2152.756 2075.809 2072.103 2090.901
## 2020 2116.239 2115.192 2131.200 2131.971 2128.060
## 2021
ggplot(data=newsalfullrows,aes(x= factor(year),y= mon_wage,fill= sex))+geom_bar(stat="identity",position= "dodge")+labs(title="Monthly salary of both male and female",x="year",y="Monthly salary")

ggplot(data=newsalfullrows,aes(x=EmpTotal,y=mon_wage,color=sex))+geom_point()+geom_smooth(color="black")+labs(title="Monthly salary vs No of employees",x="Monthly salary", y="No of Employees")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggplot(data=newracefullrows,aes(x= year,y= EarnS,color= race))+geom_point()+geom_smooth(color="black")+scale_y_continuous(limit=c(0,20000))
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 20 rows containing non-finite values (stat_smooth).
## Warning: Removed 20 rows containing missing values (geom_point).

ggplot(data=newracefullrows,aes(x= year,y= EarnS))+geom_bar(stat="identity",position="dodge",aes(fill= race))+scale_y_continuous(limit=c(0,20000))
## Warning: Removed 20 rows containing missing values (geom_bar).

subsetting male average salary

library('ggplot2')
library('forecast')
library('tseries')
#newsal <- drop_na(Sal)
newsalfullrows$year <- as.Date(newsalfullrows$year)
nsal <- subset(newsalfullrows, sex == "Male", select = c(mon_wage, year,sex,industry))
head(nsal)
dd <- aggregate(nsal$mon_wage ,by=list(dept = nsal$industry), FUN = mean)
head(dd)
sorted <- dd[order(-dd$x),]
topSorted <- sorted[c(1:3),]


#plot : Males who earn highest in top departemnts
ggplot(data =topSorted, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "blue")+labs(x = "Department", y = "salary for Male",title = "Males who earn highest in top departemnts")

# Bottom 3 departments who earn lowest
bsorted <- dd[order(dd$x),]
botSorted <- bsorted[c(1:3),]
#plot : Males who earn lowest in bottom departemnts
ggplot(data =botSorted, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "dark green")+labs(x = "Department", y = "salary for Male",title = "Males who earn lowest in bottom departemnts")+theme(axis.text.x = element_text(angle=45,hjust = 1))

#subsetting female average salary

fnsal <- subset(newsalfullrows, sex == "Female", select = c(mon_wage, year,industry,sex))
head(fnsal)
dd1 <- aggregate(fnsal$mon_wage ,by=list(dept = fnsal$industry), FUN = mean)
head(dd1)
sorted1 <- dd1[order(-dd1$x),]
topSorted1 <- sorted1[c(1:3),]
#plot : Females who earn highest in top departemnts
ggplot(data =topSorted1, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "pink")+ labs(x = "Department", y = "salary for Female",title = " Females who earn highest in top departemnts")

# Bottom 3 departments who earn lowest for female
bfsorted <- dd1[order(dd1$x),]
botfSorted <- bfsorted[c(1:3),]
#plot : Males who earn lowest in bottom departemnts
ggplot(data =botfSorted, aes(x= dept, y= x) )+geom_bar(stat = "identity",fill = "violet")+labs(x = "Department", y = "salary for Female",title = "females who earn lowest in bottom departemnts")+theme(axis.text.x = element_text(angle=45,hjust = 1))

#Subsetting for male and female who earn highest in top 8 departments

nsal1 <- subset(newsalfullrows, sex == "Male" | sex == "Female", select = c(mon_wage, year,industry,sex))
head(nsal1)
dd2 <- aggregate(nsal1$mon_wage ,by=list(dept = nsal1$industry,sex= nsal1$sex), FUN = mean)
head(dd2)
sorted2 <- dd2[order(-dd2$x),]
topSorted2 <- sorted2[c(1:10),]
ggplot(data =topSorted2, aes(x= dept, y= x,fill= sex) )+geom_bar(stat = "identity",position ="dodge")+labs(x = "Department", y = "Average salary",title = "Males and Females who earn highest in top 8 departemnts ")+theme(axis.text.x = element_text(angle = 45,hjust=1))

sorted3 <- dd2[order(dd2$x),]
bSorted3 <- sorted3[c(1:10),]
ggplot(data =bSorted3, aes(x= dept, y= x,fill= sex) )+geom_bar(stat = "identity",position ="dodge")+labs(x = "Department", y = "Average salary",title = "Males and Females who earn lowest in top 8 departemnts ")+theme(axis.text.x = element_text(angle = 45,hjust=1))

newsal <- drop_na(Sal)

loading average salary male and females:dataset 2(Average salary)

library(forecast)
library(readr)
newsal$avg_wage_ft <- as.numeric(newsal$avg_wage_ft)
library(zoo)
newsal$Date <- paste (newsal$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata1 <- ts(newsal$avg_wage_ft,frequency = 12)
tsdata1
##           Jan       Feb       Mar       Apr       May       Jun       Jul
## 1    55458.50  86682.40  54431.80  85707.40  55109.50  85267.30  18393.40
## 2    99920.40 178424.00 127523.00 175534.00 128534.00 177805.00  31223.40
## 3    58112.50  66271.40  56859.20  70643.50  56820.50  74757.40  60364.50
## 4    53526.10  63128.90  54962.60  59704.90  55028.40  63631.80  83664.60
## 5    43737.20  70865.60  44096.90  57713.90  46257.90  57285.50  33057.80
## 6    42522.10  65784.40  45328.70  70146.20  46605.20  72558.50  33219.20
## 7    79105.70  81777.00  73835.60  84362.90  75047.70  84184.80  40489.90
## 8    53670.20 114071.00  54758.00 117501.00  71981.80 121559.00  39549.50
## 9    21655.00  35993.20  20995.30  30974.90  24421.60  56441.90  31216.40
## 10   27280.20  43099.00  30139.00  35946.40  31073.30  43311.30  31448.70
## 11   48093.40  62677.00  47688.70  64969.30  49037.10  66329.80  52731.10
## 12  108388.00 115709.00 109712.00 120292.00 105819.00 122867.00  45350.90
## 13   39494.80  58937.00  36048.70  60610.60  32984.60  55121.80  66644.30
## 14   40377.10  68698.10  37365.50  68460.00  37426.70  73682.90  68732.10
## 15   64332.90  73956.70  75341.10  79193.40  74997.10 104016.00  22098.40
## 16   27475.10  37987.60  27695.60  39227.20  28458.80  40428.60  31610.70
## 17   35086.40  35455.50  45162.80  34173.20  44099.10  36675.70  40037.90
## 18   28303.80  28284.00  29367.60  40103.30  43058.00  39944.20  42790.00
## 19   28776.30  24135.80  27830.30  18904.00  26104.10  20467.70  25778.40
## 20   32814.80  28655.30  34589.20  33819.20  34664.70  34310.50  34418.10
## 21   47352.10  32984.30  46781.80  53531.90  57650.40  52171.90  55980.80
## 22   48361.30  42067.60  45596.10  82364.60 117369.00  81726.20 198142.00
## 23   36745.80  41292.60  36745.20  42417.60  37645.00  43499.10  31244.30
## 24   48312.50  45628.70  48296.30  45393.80  48689.80  32468.70  52017.90
## 25   73451.00  74795.70  79621.50  67236.50  73783.00  28905.00  40348.10
## 26   31949.10  26452.10  30396.10  26328.70  30816.10  47082.80  72404.30
## 27   31178.10  23831.40  30338.30  24716.00  31498.20  37122.20  77211.20
## 28   51424.40  20025.30  31872.40  19912.70  33266.50  33303.60  35392.40
## 29   32947.30  31594.00  31612.00  30774.80  32333.10  20562.00  29171.10
## 30   27833.70  21384.80  28314.80  21870.20  28807.70  30289.30  31249.60
## 31   34437.90  28188.00  34740.20  30208.60  34290.90  79828.50 103558.00
## 32   46751.60  23489.80  47027.00  27868.60  46231.00  47186.70  49411.20
## 33   71998.50  58387.50  69198.40  62070.00  69581.20 105175.00 147386.00
## 34   22086.00  19992.10  21086.80  20871.90  21237.60  50021.20  95376.10
## 35   90281.20  69943.50  89507.80  71937.80  93124.60  50403.00  57772.80
## 36   25126.90  21922.60  24283.20  21552.70  25006.70  40946.90  50389.30
## 37   53101.60  46479.80  50188.00  46874.10  52099.30  21996.70  30748.90
## 38   20940.80  19315.40  22047.40  19958.50  22863.50  59180.90  80767.10
## 39  109032.00  71827.30 104883.00  71994.90 105495.00  46794.00  62873.40
## 40   72414.50  56704.30  70993.60  58544.10  67459.30  54070.70  90825.90
## 41   98374.30  88448.00 100899.00  86841.20 103552.00  39206.40  42945.60
## 42   78799.30  60543.50  76532.70  66690.70  81124.70  83858.90  86707.50
## 43   42615.00  33239.50  42966.10  34427.80  42349.40  68039.40  78912.60
## 44   53489.30  48602.60  53279.70  51058.20  53781.80  68051.40  80560.80
## 45   43224.20  38836.20  43356.20  36705.70  43244.00  47365.60  70464.30
## 46   54163.90  40154.90  52371.40  39674.30  53941.70  27985.80  36532.00
## 47   28939.00  29296.40  29854.10  29635.60  33108.80  64519.40  74143.00
## 48   46767.50  45848.90  46138.00  41185.20  46418.60  35397.80  39053.40
## 49   22975.20  20753.90  22900.50  21132.00  23543.40  29929.10  34313.30
## 50   67338.40  41775.90  66080.70  49642.30  63924.40  41559.30  42698.80
## 51   38262.10  31284.10  35025.50  31681.70  39610.50  13036.90  17206.60
## 52   42302.20  28110.70  41039.40  31344.10  39008.10  33183.40  51499.00
## 53   48498.60  48604.00  63359.80  66689.30  60697.00  67387.50  58633.50
## 54   41969.20  67369.30  56849.20  74121.90  53925.50  77619.80  57143.10
## 55   20057.10  23820.50  27276.50  30990.90  27416.80  31718.40  28150.10
## 56   30522.90  37069.30  24578.60  25750.50  24211.20  26901.10  20018.80
## 57   26253.10  64308.40  30481.80  32571.00  29974.60  34649.50  30044.90
## 58   68362.70  83640.60  29634.10  41768.90  30393.20  41615.20  30670.50
## 59   52128.30  74604.80 145786.00 166196.00 153984.00 181351.00 157053.00
## 60   57316.40  45949.00  57447.30  45763.60  56718.50  58566.70  73800.00
## 61   55437.20  49924.20  53795.50  50093.10  58516.00  40905.50  47055.80
## 62   41974.20  39524.30  41757.60  37177.00  47655.60  14482.40  17772.50
## 63   37704.50  32520.70  37518.10  32828.80  39116.90  24027.70  33005.80
## 64   47082.30  38483.70  47294.80  39766.30  48116.60  40090.40  42263.30
## 65   38292.40  31577.30  38937.50  19806.60  24943.20  18501.80  25056.90
## 66   42113.70  11245.80  48268.80  78031.20  79033.30  64595.20  98212.30
## 67   49621.80  47054.30  48605.60  59811.90  81918.30  59807.80  79195.10
## 68   41058.50  29181.80  42959.10  82476.30  90671.60  83972.40  89170.80
## 69   51334.00  44163.60  48570.60  27575.80  58145.50  27722.20  58681.00
## 70   26530.60  26093.60  26230.40  35365.50  40313.60  25923.30  39379.80
## 71   26470.20  22839.70  24371.50  30600.90  38832.00  26359.70  38817.70
## 72   46796.30  43366.10  46241.10  71574.80  85748.90  50835.30  73100.40
## 73   50174.70  37505.10  52365.10  27644.00  33733.50  28776.20  32303.80
## 74   59945.50  42538.50  54194.90  42442.70  50799.70  42512.50  49596.70
## 75   28212.50  39203.80  44065.80  57192.40  43773.70  57424.50  44747.40
## 76   32332.50  41947.70  64518.90  72987.60  62123.40  72236.70  62268.50
## 77   49982.70  68058.00  25729.40  35305.30  31905.50  34485.90  30293.60
## 78   41465.30  42585.70  18837.30  35529.80  18307.40  31803.80  18242.10
## 79   26768.80  29666.00  54454.00  60794.50  57619.60  60848.80  52386.20
## 80   31660.20  34355.90  31367.40  32555.60  30636.90  31251.20  64880.00
## 81   54697.70 169056.00  51167.50 162353.00  54359.70 186121.00  65889.90
## 82   57784.00  69936.90  63806.10  69457.40  67449.90  68128.30  66083.00
## 83   66211.20  67142.20  62673.80  65911.90  61867.60  66242.30  51786.70
## 84   53013.20  53132.30  52889.40  53128.60  51918.80  57329.20  26136.10
## 85   90773.00  92230.70  77126.70  87517.40  74819.70  88172.00  27225.70
## 86   53675.70  63776.20  54251.90  63217.30  62297.70  65117.60  30190.70
## 87   37238.70  37257.90  36209.70  39369.60  37008.70  40531.60  51983.90
## 88   58332.70  79554.70  57048.90  81774.20  59867.40  83199.10  43367.20
## 89   38387.30  54065.90  37200.80  50821.20  36492.30  51803.10  65271.00
## 90   47183.40  59112.60  45294.90  59724.30  40453.00  58455.30  46268.70
## 91   38397.30  56839.40  38660.00  55665.90  39234.30  55471.40  20321.20
## 92   41736.90  54521.70  43956.70  55223.60  46272.70  56766.00  29103.40
## 93   26007.00  27846.00  26253.50  26280.20  25795.00  27351.20  17042.60
## 94   19040.30  19339.10  18842.30  19964.20  18939.60  19860.10  28827.40
## 95   21310.20  25829.10  20543.70  26847.90  21533.00  28193.80  38503.10
## 96   12645.00  31276.20  11841.20  31908.50  11962.40  31894.00  34830.10
## 97   71721.70  55342.50  69822.40  25930.80  39988.10  32272.30  33655.50
## 98   44579.60  21120.60  23179.20  27416.50  25031.60  27959.90  25347.90
## 99   49366.60  99102.10  45600.20  47039.10  42968.60  45448.30  40492.00
## 100  66642.50  96809.40  46683.60  78752.80  53889.30  67762.30  61653.30
## 101  30640.70  14648.40  23184.30  15195.00  21893.80  15228.60  24262.40
## 102  25323.00  24914.10  38987.10  25246.60  32569.10  25257.80  33331.30
## 103  33769.50  32220.40  44747.90  32220.10  49387.40  45623.00  48682.70
## 104  44294.50  24919.40  36086.30  25367.50  36551.70  25180.40  35938.10
## 105  42869.50  11059.60  42318.10  11072.30  43090.60  11212.20  44611.20
## 106  24146.50  21338.00  25052.40  20843.00  21211.70  21109.00  21782.20
## 107  22804.30  31694.70  34176.10  31250.20  34742.00  31553.50  32064.20
## 108  36511.00  18819.20  22856.10  17686.20  20585.60  17273.50  20831.50
## 109  22104.10  33151.40  45515.70  33938.50  49016.40  38018.60  49691.60
## 110  81530.90  53471.80  64026.20  53608.90  64505.00  54765.30  67854.70
## 111  45688.70  57046.00  74577.40  53723.70  73078.80  65013.40  74917.30
## 112  70774.20  30047.20  32928.00  30276.70  32840.30  31325.20  33164.20
## 113  43037.70  65044.00  79669.50  65894.00  91059.80  74213.20  96155.50
## 114  47446.70  30521.20  30678.20  31594.80  32521.10  29577.70  31214.70
## 115  44340.80  50347.80  78185.10  49188.90  75587.30  48641.80  76563.50
## 116  70280.40  29988.20  30031.90  31265.30  32453.30  31616.40  37544.50
## 117  26765.50  37854.20  38563.20  35604.70  37665.80  35665.80  41866.80
## 118  84098.70  22926.00  28134.50  23363.00  28860.60  22743.40  29274.40
## 119  31221.20 102656.00 153323.00 103134.00 155950.00 107421.00 157104.00
## 120  50641.50  24906.50  30015.50  24255.60  26714.90  25049.00  28766.20
## 121  38624.00  40209.10  45885.30  39084.60  45522.20  38954.80  44898.30
## 122  28482.00  38859.10  59762.90  38269.20  57690.60  40761.10  51806.20
## 123  40619.60  47592.00  64225.50  46210.10  61959.60  46588.00  76301.40
## 124  65700.20  39542.90  10012.60  37354.70  10139.20  38919.20  40410.30
## 125  21068.90  38234.00  18595.70  28854.50  19171.30  29221.80  24029.20
## 126  24326.40  40927.20  23842.90  40884.50  27172.40  43870.00  19319.90
## 127  25548.10  32545.30  26980.90  33030.30  27590.40  33880.70  34697.00
## 128  38993.60  43946.30  36626.60  43996.90  38812.30  69354.70  93881.10
## 129  91288.90  89259.90 105870.00  84971.80  92833.80  56030.80  77179.00
## 130 103208.00  69231.70 104354.00  71384.60 103835.00  28728.10  36401.80
## 131 115731.00  91785.80  97187.20  76672.80  93234.30  59849.00  74851.40
## 132 104462.00  70796.10 101133.00  71221.40  99247.20  27118.70  41110.60
## 133  45423.20  35841.00  43039.20  36879.40  41204.90  64251.40 101621.00
## 134  33218.60  19433.60  31268.60  32308.20  41121.20  32582.30  40054.00
## 135  52041.60  42973.60  51712.60  43950.50  59460.10  31558.20  27825.00
## 136  35915.40  39567.50  19143.70  28745.10  23097.40  30590.40  24249.20
## 137  61215.70  83952.80  46022.90  47326.70  43451.10  47874.90  44346.90
## 138  15210.90  56131.20  35222.80  64311.70  37073.30 129541.00  41991.60
## 139  16787.90  21062.50  23806.50  31014.20  24378.70  29276.40  24877.40
## 140  43037.80  55409.90  25475.30  30657.20  26835.80  32449.40  26563.60
## 141  84283.80  92511.00  25873.70  26292.30  26624.00  26792.90  23814.00
## 142  49830.20 109827.00  18816.30  20867.50  19663.70  20181.90  19730.20
## 143  42494.10  61425.30  31979.10  36704.20  34270.80  36022.70  34602.70
## 144  40704.40  44883.90  45434.70  63497.80  45642.70  64617.30  45586.40
## 145  43935.80  59168.10  65138.90  91635.20  66296.50  91495.50  67523.00
## 146  24180.00  36636.20  71072.40  80534.10  65314.90  74850.20  64494.40
## 147  34941.90  43462.90  23796.50  33617.30  22617.60  31877.40  21524.70
## 148  36235.20  44185.30  20882.30  22929.10  21786.30  22955.90  22704.30
## 149  31036.10  46920.40  23592.40  34435.20  25132.30  34004.50  25466.30
## 150  53880.40  71518.10  19060.30  23382.60  19884.80  24793.60  19480.00
## 151  35150.60  40242.60  19964.40  28472.20  18098.20  29621.10  20473.70
## 152  28301.80  40660.90  36524.70  39003.80  35615.70  39860.30  13369.50
## 153  31211.30  54121.50  23240.80  30421.30  17214.50  28029.20  11430.40
## 154  43438.60  54219.60  25140.20  29193.90  20191.70  26301.90  20446.80
## 155  15148.30  26360.50  38533.50  44992.70  33103.50  49459.80  33018.60
## 156  90391.60  91465.80  60592.20  66088.00  61850.60  66200.80  65731.90
## 157  38211.30  56186.40  54529.60  68698.50  68612.10  68846.80  74978.00
## 158  25754.90  26502.80 116304.00 186196.00 116161.00 176829.00 115829.00
## 159  92550.60 122741.00  27215.50  36492.90  26348.50  36172.70  26109.90
## 160  65149.90  72910.40  34899.90  52043.90  30870.30  47376.70  48756.60
## 161  31432.20  35915.50  30929.00  31951.00  30930.70  31230.30  31459.20
## 162  60918.40  77105.30  42967.90  51009.40  43760.20  49866.90  42158.40
## 163  88307.20 123099.00  52846.40  65982.40  56571.80  66450.10  56698.90
## 164  48650.40  51949.50  43571.10  72590.90  41979.60  70595.50  42307.60
## 165  57247.60  90578.60  41945.00  59398.40  46451.90  61327.30  53564.80
## 166  61420.50  79300.90  43932.00  65670.70  43357.10  66565.20  45490.60
## 167  40443.70  52277.40  46911.60  58631.90  45984.10  53169.20  49532.10
## 168  23059.60  35650.80  19334.30  21187.40  20872.40  21998.00  20987.90
## 169  28446.00  35674.30  20755.20  36489.20  22504.50  38251.90  22767.10
## 170  31914.70  42461.20  46733.50  73812.80  47007.40  69092.80  46877.40
## 171  21408.60  33405.20  26332.70  40163.70  25787.40  39559.10  29351.40
## 172  10223.40  28491.20  38373.70  46889.90  37999.60  48855.70  38317.80
## 173  23108.70  23476.50  24017.50  30084.70  24225.70  28021.10  24241.90
## 174  80247.40 139495.00  36413.90  37481.80  30981.80  35857.50  32819.30
## 175 124580.00 124726.00  98192.20 118203.00  96341.90 116035.00  98169.70
## 176  31503.80  32136.70  28506.40  39295.70  28944.50  33565.40  29575.10
## 177  30074.40  45581.90  23175.20  32427.90  25156.10  36552.40  28633.30
## 178  45739.20  47819.70  65298.20  83125.70  64321.00  78467.90  66105.20
## 179  73786.60 107782.00 159392.00 229096.00 162641.00 233337.00 165391.00
## 180  36043.30  37348.30  27210.20  41738.20  27138.10  40317.00  30624.90
## 181  87999.50  88551.30  51625.50  60370.80  52904.50  60370.00  53600.10
## 182  53449.80  54972.40  49772.90  57133.60  49603.80  55722.10  50585.70
## 183  48528.40  52961.20  57749.50  78722.70  57601.80  77208.00  57953.70
## 184  31894.90  85486.20  39546.70  50765.00  36751.30  52479.30  36309.40
## 185  25685.40  37028.60  19187.00  23639.90  20829.70  23872.50  23939.00
## 186  18480.90  27293.90  21967.30  33334.90  22415.50  32688.30  23524.00
## 187  22585.50  33117.80  47434.80  63222.60  49620.90  62588.50  47950.30
## 188  39376.30  41663.20  61966.20  62445.80  60724.50  65856.30  64098.00
## 189  62831.00  67480.70  37115.20  56995.30  36939.00  57334.80  38090.30
## 190  32608.80  35804.70  49455.20  78612.70  49745.70  79930.90  50495.30
## 191  60540.80  95234.00  63065.10  95693.80  77160.00  98623.80  74678.80
## 192  61742.80  95578.30  26323.50  70485.30  25911.80  50791.40  30745.40
## 193  47564.90  55830.10  65232.60  91862.10  62492.90  96020.30  63073.60
## 194  72656.80  78735.20  48270.10  50813.40  43430.30  49845.40  50231.10
## 195  54692.90  65632.70  20773.60  34663.90  20797.50  39032.10  21060.20
## 196  57948.00  76526.20  25755.90  31560.90  25753.30  32816.10  25808.10
## 197  30298.10  38117.20  43350.40 119092.00  36886.30  55356.20  39175.90
## 198  26139.10  30686.50  61454.90  70718.10  60501.50  68353.40  60642.90
## 199  34005.20  39751.00  34404.40  56332.10  35392.40  56558.80  37129.20
## 200  19027.20  25651.80  52686.30  71470.50  52162.30  71054.80  53350.60
## 201  30758.80  44295.60  45572.90  46700.70  36016.70  26621.90  59686.80
## 202  36559.60  29237.90  42329.90  29358.10  39826.40  36867.80  52557.60
## 203 189149.00  91961.20 118336.00  99695.50 112827.00  49126.00  70888.10
## 204  77015.90  63910.70  77255.00  66644.70  79527.00  26417.60  27604.60
## 205  48520.70  45617.50  48194.00  46265.60  48187.70  35292.00  40494.50
## 206  92986.90  69643.00  92891.80  72220.40 102640.00  40517.60  47392.30
## 207  30964.80  29424.70  30144.50  29677.10  30058.60  20040.90  20890.00
## 208  35372.90  23297.70  36388.80  25503.60  37546.60  73089.20  76635.90
## 209  31404.90  27843.20  30397.90  27690.10  30310.70   8337.42  32737.80
## 210  33336.90  10075.90  35445.30  56913.10  81716.10  55689.10  78920.50
## 211  40196.50  38829.90  40323.10  42280.20  47064.40  42157.40  46718.30
## 212  84648.40  71368.60  86606.00  40384.20  43021.80  37094.80  41589.80
## 213  67482.10  60623.30  62392.30  44549.40  48823.60  40484.70  43587.90
## 214  47785.90  36676.30  47750.30  27514.90  27640.20  26849.70  26992.10
## 215  31813.00  20025.30  35351.20  20278.30  34929.50  35841.30  37961.80
## 216  51411.30  44249.80  53066.00  44316.00  51574.20  38018.60  40437.10
## 217  51848.60  47913.00  53466.00  54422.70  62237.30  28028.00  39850.60
## 218  29322.70  28720.00  32327.80  29191.10  29358.80  42897.90  73259.60
## 219  59420.40  37346.20  61474.40  36901.50  59139.90  24029.60  30658.40
## 220  31692.40  21498.60  31043.60  22504.20  31319.90  51975.20  62947.20
## 221  46056.40  42027.80  47831.90  41292.50  44274.20  24739.00  26811.10
## 222  34543.80  25808.60  36322.80  25231.50  37546.60  33252.40  41355.00
## 223  28740.70  27196.70  28144.90  27462.10  28366.40  26321.90  39789.20
## 224  61512.90  62290.20   2002.53   2027.83  49199.10  41721.80 105603.00
## 225  38502.30  27320.10  50883.30  26247.00  48650.90  66966.40  83414.50
## 226  57278.90  52892.70  56145.60  52604.60  59369.60  28917.50  31673.50
## 227  96525.50  56756.50  94786.60  66103.40  85413.40  35360.50  42773.80
## 228  56818.20  51525.70  55409.00  51108.00  51822.60  37084.80  43913.30
## 229  31509.90  25370.70  28937.80  28884.60  29424.50  65047.80  77743.00
## 230  20587.60  19530.00  20527.40  12707.00  24554.60  86300.00 131111.00
## 231  27128.60  23047.80  25399.10  23029.10  27583.10  21497.60  25065.70
## 232  51009.60  46019.80  46707.10  43031.20  47588.50  50820.70  64753.80
## 233  35983.70  27159.60  36495.40  28285.40  36839.20  22150.80  37276.80
## 234  51464.50  43028.50  52686.40  41019.40  50023.60  20480.70  24908.80
## 235  34129.40  33181.10  34563.40  34022.40  34160.10  46394.70  65888.30
##           Aug       Sep       Oct       Nov       Dec
## 1    51929.70  18262.40  49598.00  17693.80  50787.10
## 2    48778.20  31693.80  33370.20  35615.50  36439.60
## 3   103035.00  56464.60 101524.00  58797.30  95358.60
## 4    92254.20  78976.00  91961.30  84087.90  90847.90
## 5    39245.60  40732.10  42177.00  37884.60  45607.10
## 6    46509.80  36176.50  46152.10  35251.20  49015.60
## 7    50066.80  28436.40  49178.10  25723.20  50804.80
## 8    65598.40  38154.10  43861.10  44481.30  45188.80
## 9    33578.50  31982.60  33891.00  30460.30  37167.80
## 10   84891.40  33105.90  74257.20  48879.90  78098.90
## 11   80998.70  50930.70  87303.10  54658.70  88056.70
## 12   57660.30  46966.60  60639.70  38996.70  69569.70
## 13  103141.00  66917.60 101416.00  76335.20 112492.00
## 14   86373.20  50789.70  81579.80  50651.90 100890.00
## 15   24155.60  21858.30  22663.70  21117.80  28080.20
## 16   33793.10  37375.50  32432.10  35091.30  33178.20
## 17   35999.00  39530.40  25782.00  27276.60  28018.50
## 18   39492.30  43574.00  24091.30  27590.80  23443.40
## 19   19732.10  25296.80  26011.40  32622.80  25877.50
## 20   35284.10  35595.50  31778.70  39017.60  32064.00
## 21   48746.10  57691.00  36047.70  44524.00  36835.40
## 22   84542.20 200646.00  39408.50  46527.70  44432.00
## 23   15019.00  31776.50  15208.70  32202.20  47421.00
## 24   32301.00  49409.40  51008.60  70826.00  65350.60
## 25   31873.40  42018.60  33441.30  43191.60  27245.70
## 26   48517.50  83228.30  51215.50  84241.30  23718.40
## 27   52029.70  54614.50  56159.50  73333.40  32350.60
## 28   29171.00  38086.00  31718.10  36835.10  32338.50
## 29   31924.30  33965.80  23803.30  31689.90  21265.40
## 30   28789.30  31285.60  28829.30  28835.50  29168.90
## 31   81590.00  91860.60  87485.30  88303.60  29178.60
## 32   43281.10  50732.00  43921.10  49294.30  59287.10
## 33  112080.00 153295.00 117377.00 158343.00  19860.30
## 34   48687.00  92031.90  47785.50  94479.60  68442.60
## 35   49642.10  57149.90  51175.10  57118.50  21139.80
## 36   38707.40  49619.10  37939.10  50710.70  46136.60
## 37   29469.50  42596.20  32683.90  42046.80  19525.40
## 38   35994.70  63960.60  30185.40  65244.80  72441.00
## 39   48432.30  71001.50  49956.10  57706.10  56283.50
## 40   73954.60  82935.60  85342.70 132464.00  89686.10
## 41   32147.60  43810.60  32373.60  45403.70  56444.90
## 42   85473.30  85720.50  84761.70  85178.40  33633.50
## 43   68086.00  79447.50  72914.80  80712.00  48508.50
## 44   67947.20  79440.50  66647.10  79188.90  42875.40
## 45   46249.70  77265.80  45416.00  71734.70  41998.40
## 46   36569.90  37239.10  37206.30  38719.20  27723.70
## 47   60732.90  73275.00  58532.10  73006.50  42454.40
## 48   31596.60  35651.30  30480.10  31575.30  21261.80
## 49   30127.70  35388.20  31161.50  37202.00  39913.10
## 50   41878.50  42222.90  42147.90  44373.70  30875.60
## 51   14865.40  16789.30  17244.80  18436.60  30263.10
## 52   33157.60  49107.10  33298.90  49695.50  49514.10
## 53   67797.20  43191.80  59713.70  43647.20  63189.80
## 54   83428.20  22600.30  23097.10  20668.30  21981.20
## 55   33053.30  30404.70  37846.10  30157.30  37521.30
## 56   28398.60  23847.80  26718.90  26559.50  35136.80
## 57   33951.90  63856.70  88119.10  66664.10  85288.50
## 58   46837.50  47217.00  72076.70  48606.80  66366.70
## 59  188445.00  57847.30  71230.20  73794.60  45177.80
## 60   59261.60  73958.10  59506.10  75544.30  50092.90
## 61   43854.50  46525.70  44429.00  50687.60  40319.90
## 62   14134.80  17978.70  15217.70  18219.70  32227.90
## 63   24394.70  36575.80  28566.10  41351.50  37826.90
## 64   31235.20  43334.10  30696.30  37882.50  30225.60
## 65   18463.50  25056.70  11092.70  39829.40  11105.40
## 66   56348.00  98100.20  49747.90  55378.70  47221.80
## 67   59015.80  80288.80  39830.50  56298.70  39104.50
## 68   80268.20  92463.00  49710.80  51710.40  47521.10
## 69   47435.90  62440.50  23891.90  27235.20  24711.40
## 70   25757.10  40005.10  22528.80  27360.30  22554.70
## 71   26630.10  38870.10  44033.70  47653.40  43388.80
## 72   19568.60  71160.30  38347.40  49887.60  38800.70
## 73   30795.10  32626.20  48646.10  59773.80  54293.70
## 74   39710.60  50141.60  26259.30  40254.20  36792.10
## 75   56850.10  38568.30  43975.60  35362.00  43741.50
## 76   65563.50  49200.30  69772.60  49381.10  71148.20
## 77   30326.40  33400.80  42936.80  20427.00  41958.60
## 78   29378.30  24016.40  34691.70  24047.70  31971.60
## 79   67352.20  36937.30  35146.90  24383.60  33606.80
## 80   90036.10  64752.50  86025.70  67484.60  87692.30
## 81   99077.80  68109.60 103275.00  69704.80 109630.00
## 82   67162.80  59464.80  66509.70  57604.50  68395.50
## 83   52900.60  50318.10  53914.80  46779.70  56175.90
## 84   36980.00  25005.00  38313.50  25811.70  40700.70
## 85   36164.10  25615.40  36701.30  25918.40  36763.30
## 86   40087.40  29806.40  39635.20  29122.90  40276.60
## 87   57296.10  53697.60  56253.90  55863.70  57913.60
## 88   55880.40  43368.90  56741.20  44344.10  58236.60
## 89   74759.80  63217.50  73648.90  63771.40  77621.50
## 90   52263.00  41265.30  53814.40  41293.20  53063.00
## 91   48452.60  19867.40  46843.30  21056.60  44963.60
## 92   34256.80  26890.50  30314.20  21879.70  27900.80
## 93   20754.70  19280.40  20981.50  18703.40  20546.20
## 94   34903.90  25594.80  35881.10  28154.70  37091.70
## 95   54735.80  39001.30  53199.50  39882.20  54323.80
## 96   34922.00  26400.80  53126.70  85468.60  51363.70
## 97   30924.80  42464.00  29810.50  43468.70  26356.70
## 98   26786.60  43466.10 103049.00  46518.20  98352.80
## 99   48682.00  69208.90 100513.00  67159.50  96896.20
## 100  71304.50  26796.50  32646.40  26827.30  30557.10
## 101  21971.70  23692.60  21490.90  23937.60  20983.10
## 102  27304.00  27506.60  26470.00  29128.70  24553.30
## 103  32729.20  42965.60  32943.40  42954.20  33363.20
## 104  38381.50  38649.80  38888.30  45542.00  39507.50
## 105  21882.70  28537.10  12015.20  28350.90  12167.00
## 106  19555.10  22673.50  19264.00  22217.90  21789.70
## 107  13905.60  37039.80  10426.90  38743.40  10608.90
## 108  21427.30  21596.60  21610.40  21768.00  21547.10
## 109  70888.00  84656.60  73787.30  83469.00  76079.90
## 110  33768.80  45245.40  36982.80  45371.00  36792.70
## 111  62761.20  77615.90  62109.00  73415.40  62595.40
## 112  37225.90  41171.50  35787.20  42398.70  35529.60
## 113  31753.60  47166.60  32532.30  47217.70  32427.10
## 114  35159.90  45330.60  34415.00  45412.00  34691.00
## 115  51192.40  72410.10  53159.90  70333.40  54865.70
## 116  20464.80  26188.60  21009.70  25822.20  21498.50
## 117  43280.60  73596.50  45208.90  86807.40  42926.90
## 118  19565.10  27608.40  19352.10  28021.10  18335.70
## 119  45005.90  50781.70  45362.30  51502.50  45901.80
## 120  30919.30  45321.40  33014.30  40038.30  31277.70
## 121  25939.30  31855.70  24467.50  31135.60  24596.10
## 122  25840.20  38250.30  25725.70  39712.80  25826.40
## 123  47614.20  70493.40  47846.50  63955.50  46614.70
## 124  60730.00  39999.20  59501.80  42398.20  59496.30
## 125  32541.20  32866.80  35036.60  34086.10  34463.50
## 126  20660.20  19528.80  20452.40  19903.70  22551.70
## 127  38566.60  35075.00  37793.90  38656.90  40466.50
## 128  68956.50  93540.20  68223.50  94058.70  88881.90
## 129  56325.10  78660.90  60990.10  81498.30  69021.30
## 130  30565.80  36205.50  30637.10  38226.30  87316.90
## 131  55267.50  75620.60  61637.50  77534.20  69468.30
## 132  27429.00  38958.30  28153.00  37198.30  35795.40
## 133  62439.30  97453.20  67023.60  96032.80  24988.60
## 134  31183.30  38775.20  33116.70  38665.00  44686.80
## 135  31823.10  35634.70  36304.40  35606.40  37132.40
## 136  32243.60  68566.70  82091.10  77740.30  81469.20
## 137  48625.50  14622.20  50789.10  14961.90  51647.80
## 138 125226.00  16481.60  19745.40  16812.80  20473.60
## 139  29120.00  40529.50  48432.20  40698.60  49030.40
## 140  34469.40  78392.40  88146.70  82557.00  87971.40
## 141  26404.60  42957.10 108332.00  45264.90 108457.00
## 142  21097.00  42707.20  65168.70  43057.90  63403.40
## 143  36500.00  36063.00  36111.60  32005.50  36141.60
## 144  63938.70  39725.50  54519.20  41078.30  54846.30
## 145  91472.40  26827.70  42395.80  22778.00  41401.30
## 146  65840.80  39492.60  44462.10  36278.70  42578.30
## 147  31537.70  36175.00  44984.30  35412.70  43175.40
## 148  23829.80  28922.10  45642.40  30629.70  44860.50
## 149  34996.10  54216.80  75530.70  57300.50  78231.30
## 150  26407.50  41848.00  43489.70  40485.80  41335.30
## 151  29649.00  29790.90  32108.50  28436.90  32213.90
## 152  41121.60  31334.50  44526.30  31960.50  45259.10
## 153  28415.00  37002.30  52387.70  37192.20  54299.70
## 154  23602.50  19361.40  23047.20  15882.00  23228.00
## 155  55744.70  98111.70 102568.00  85345.00 101909.00
## 156  68412.80  39191.10  46663.70  37757.80  50885.80
## 157  75470.70  25201.50  26936.30  25433.10  26640.70
## 158 161803.00  96544.30 126986.00  93605.40 134541.00
## 159  36820.30  66030.60  67022.70  64068.10  68089.80
## 160  49627.70  31493.70  36772.50  31399.10  36803.90
## 161  31535.40  58829.80  77942.20  58854.00  77874.20
## 162  51946.50  86990.80 115425.00  87685.40 104093.00
## 163  67952.20  48165.70  55466.40  49668.00  55862.00
## 164  73104.80  54842.60  86151.40  55151.60  97298.20
## 165  62785.20  58427.60  69147.70  59765.20  70347.40
## 166  67197.40  39746.20  52563.90  40028.20  52640.70
## 167  49784.70  23316.50  29690.40  22575.70  34477.80
## 168  21967.70  27289.40  28496.40  27548.70  32750.90
## 169  40868.30  20898.10  40579.00  30135.30  39459.70
## 170  70155.00  18473.10  23523.10  22007.00  22320.70
## 171  36153.20  19557.10  29724.30  10095.80  26996.20
## 172  49591.00  21763.70  23912.60  21924.20  24662.00
## 173  27602.10  78925.60 133563.00  75475.80 135691.00
## 174  36431.50 122884.00 156850.00 106177.00 123026.00
## 175 115308.00  30991.60  35139.20  30222.80  39391.90
## 176  35422.70  30794.30  37329.40  30791.90  40296.60
## 177  41630.80  42423.20  42832.10  44836.30  44909.50
## 178  78976.30  62565.90  97438.00  65447.20  96980.20
## 179 240996.00  31992.00  37303.40  31839.80  37069.90
## 180  32743.40  71371.50  78498.10  75548.70  79683.20
## 181  61429.70  53477.30  56585.60  53574.50  56598.80
## 182  55623.30  47079.40  54214.10  48046.00  52683.00
## 183  80834.70  42102.90  80505.70  35338.10  81886.80
## 184  52415.20  26414.70  39673.10  26455.80  37468.80
## 185  24644.20  18790.20  33520.60  17723.20  30893.00
## 186  33803.90  24412.10  31798.80  22233.20  32036.70
## 187  63386.00  41401.90  42105.20  40207.20  40895.10
## 188  68329.80  56998.00  62990.40  61947.30  67181.20
## 189  62627.60  31388.90  37559.50  26236.00  34060.50
## 190  79201.50  60043.40  85656.30  59518.80  85346.90
## 191 108372.00  52935.40  84312.70  54219.80  83414.40
## 192  48689.70  46430.30  56154.40  46531.80  55049.30
## 193  93227.90  71460.60  86599.80  67648.80  74588.30
## 194  52244.40  59055.00  59584.50  56869.60  57822.20
## 195  45463.70  51520.40  71812.30  53331.20  74868.30
## 196  33120.40  28741.30  40437.40  30894.70  41083.80
## 197  56290.50  28439.30  30957.00  24520.70  28670.00
## 198  70181.70  30114.50  40862.80  33036.20  40150.20
## 199  45031.70  19448.90  23531.50  21644.50  23705.00
## 200  71435.20  31410.50  44552.70  30901.20  43959.60
## 201  23380.90  64990.90  24874.40  49089.50  28454.30
## 202  34404.40  59071.10  46513.90  55881.80 103488.00
## 203  50587.70  72825.90  53688.10  75435.70  62703.80
## 204  16322.10  28672.10  13392.30  26326.00  46230.60
## 205  34937.20  39481.60  35294.40  40109.80  66471.30
## 206  39486.10  43807.30  41330.30  41528.60  29153.50
## 207  21389.60  21878.40  22031.60  22690.50  18685.60
## 208  63339.90  72954.70  47373.40  73596.70  28652.40
## 209  19315.70  34691.00  24182.10  30456.70  35646.20
## 210  56493.40  73701.60  34210.80  38050.70  35481.30
## 211  42655.20  44846.90  73418.10  86405.50  70718.10
## 212  38312.20  41181.60  60677.80  74263.20  61860.70
## 213  39999.30  45325.00  33971.70  50804.30  35268.20
## 214  25825.50  26650.40  33908.00  33533.00  35975.00
## 215  27909.20  35882.50  26791.60  36335.90  44506.10
## 216  39532.40  42230.80  40611.80  41688.20  44978.80
## 217  28053.10  48039.20  28614.70  37740.50  27354.60
## 218  45091.60  70966.00  40793.10  67876.00  36347.70
## 219  24677.10  30909.20  25195.80  30817.40  20973.70
## 220  49255.70  59380.90  53811.40  70225.90  42419.80
## 221  24680.80  28481.20  23879.80  25263.60  26495.00
## 222  36906.30  42855.90  32586.00  44779.30  27694.10
## 223  27099.90  43560.80  23105.10  42434.70  61442.20
## 224  32824.10  56308.40  52655.60  53641.00  31240.20
## 225  66335.10  77271.40  67948.40  71697.00  52370.60
## 226  23506.70  28607.40  24000.20  27963.20  53991.40
## 227  30450.00  46422.40  37793.90  44270.00  53860.50
## 228  36251.20  48615.50  37058.50  44032.10  28033.70
## 229  64813.20  77313.90  61534.40  77985.00  18880.60
## 230  81814.50 126741.00  82072.90 121776.00  26353.70
## 231  21669.90  25331.60  22742.30  26534.10  47119.10
## 232  44322.00  59487.30  47105.20  57384.80  29327.90
## 233  24043.70  36515.10  25469.70  35989.90  44678.60
## 234  20504.30  24751.20  20763.40  27287.70  32879.60
## 235  45873.90  67087.00  45781.60  72430.40
ddata1 <- decompose(tsdata1, "multiplicative")
plot(ddata1)

plot(ddata1$seasonal)

plot(ddata1$trend)

plot(newsal$avg_wage_ft,type ="l",main = "Average salary of both genders",xlab= "Year",ylab="Average Salary")

class(tsdata1)
## [1] "ts"

Arima model

mymodel1 <- auto.arima(tsdata1)
mymodel1
## Series: tsdata1 
## ARIMA(4,0,4)(2,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ar2      ar3     ar4      ma1     ma2     ma3     ma4
##       0.5324  -0.1580  -0.0477  0.0974  -0.0894  0.7613  0.0705  0.5717
## s.e.  0.0346   0.0395   0.0478  0.0382   0.0294  0.0295  0.0289  0.0292
##         sar1    sar2       mean
##       0.0939  0.0394  48299.046
## s.e.  0.0220  0.0197   1352.806
## 
## sigma^2 estimated as 241847956:  log likelihood=-31204.48
## AIC=62432.96   AICc=62433.08   BIC=62504.29

fitting model

auto.arima(tsdata1,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 62683.07
##  ARIMA(0,0,0)            with non-zero mean : 65316.82
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 63711.67
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 64602.49
##  ARIMA(0,0,0)            with zero mean     : 69530.31
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 62729.14
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 62687.61
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 62641.8
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 62641.47
##  ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 62938.67
##  ARIMA(2,0,1)(2,0,0)[12] with non-zero mean : 62646.66
##  ARIMA(3,0,2)(2,0,0)[12] with non-zero mean : 62620.82
##  ARIMA(3,0,2)(1,0,0)[12] with non-zero mean : 62655.7
##  ARIMA(3,0,2)(2,0,1)[12] with non-zero mean : 62620.9
##  ARIMA(3,0,2)(1,0,1)[12] with non-zero mean : 62651.38
##  ARIMA(3,0,1)(2,0,0)[12] with non-zero mean : 62638.49
##  ARIMA(4,0,2)(2,0,0)[12] with non-zero mean : 62615.38
##  ARIMA(4,0,2)(1,0,0)[12] with non-zero mean : 62678.02
##  ARIMA(4,0,2)(2,0,1)[12] with non-zero mean : Inf
##  ARIMA(4,0,2)(1,0,1)[12] with non-zero mean : 62597.9
##  ARIMA(4,0,2)(0,0,1)[12] with non-zero mean : 62704.74
##  ARIMA(4,0,2)(1,0,2)[12] with non-zero mean : 62595.29
##  ARIMA(4,0,2)(0,0,2)[12] with non-zero mean : 62705.22
##  ARIMA(4,0,2)(2,0,2)[12] with non-zero mean : Inf
##  ARIMA(3,0,2)(1,0,2)[12] with non-zero mean : 62652.8
##  ARIMA(4,0,1)(1,0,2)[12] with non-zero mean : 62617.43
##  ARIMA(5,0,2)(1,0,2)[12] with non-zero mean : 62473.82
##  ARIMA(5,0,2)(0,0,2)[12] with non-zero mean : 62522.88
##  ARIMA(5,0,2)(1,0,1)[12] with non-zero mean : 62471.83
##  ARIMA(5,0,2)(0,0,1)[12] with non-zero mean : 62521.76
##  ARIMA(5,0,2)(1,0,0)[12] with non-zero mean : 62472.17
##  ARIMA(5,0,2)(2,0,1)[12] with non-zero mean : 62450.5
##  ARIMA(5,0,2)(2,0,0)[12] with non-zero mean : 62450.12
##  ARIMA(5,0,1)(2,0,0)[12] with non-zero mean : 62589.76
##  ARIMA(5,0,3)(2,0,0)[12] with non-zero mean : 62416.7
##  ARIMA(5,0,3)(1,0,0)[12] with non-zero mean : 62437.74
##  ARIMA(5,0,3)(2,0,1)[12] with non-zero mean : 62416.72
##  ARIMA(5,0,3)(1,0,1)[12] with non-zero mean : 62437.48
##  ARIMA(4,0,3)(2,0,0)[12] with non-zero mean : 62519.53
##  ARIMA(5,0,4)(2,0,0)[12] with non-zero mean : 62369.54
##  ARIMA(5,0,4)(1,0,0)[12] with non-zero mean : 62422.97
##  ARIMA(5,0,4)(2,0,1)[12] with non-zero mean : 62371.23
##  ARIMA(5,0,4)(1,0,1)[12] with non-zero mean : 62421.6
##  ARIMA(4,0,4)(2,0,0)[12] with non-zero mean : 62368.15
##  ARIMA(4,0,4)(1,0,0)[12] with non-zero mean : 62456.66
##  ARIMA(4,0,4)(2,0,1)[12] with non-zero mean : 62369.51
##  ARIMA(4,0,4)(1,0,1)[12] with non-zero mean : 62456.13
##  ARIMA(3,0,4)(2,0,0)[12] with non-zero mean : 62374.23
##  ARIMA(4,0,5)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(3,0,3)(2,0,0)[12] with non-zero mean : 62554.78
##  ARIMA(3,0,5)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(5,0,5)(2,0,0)[12] with non-zero mean : Inf
##  ARIMA(4,0,4)(2,0,0)[12] with zero mean     : 62664.08
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(4,0,4)(2,0,0)[12] with non-zero mean : 62432.96
## 
##  Best model: ARIMA(4,0,4)(2,0,0)[12] with non-zero mean
## Series: tsdata1 
## ARIMA(4,0,4)(2,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ar2      ar3     ar4      ma1     ma2     ma3     ma4
##       0.5324  -0.1580  -0.0477  0.0974  -0.0894  0.7613  0.0705  0.5717
## s.e.  0.0346   0.0395   0.0478  0.0382   0.0294  0.0295  0.0289  0.0292
##         sar1    sar2       mean
##       0.0939  0.0394  48299.046
## s.e.  0.0220  0.0197   1352.806
## 
## sigma^2 estimated as 241847956:  log likelihood=-31204.48
## AIC=62432.96   AICc=62433.08   BIC=62504.29

Residuals

library(tseries)
adf.test(mymodel1$residuals)
## Warning in adf.test(mymodel1$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel1$residuals
## Dickey-Fuller = -13.335, Lag order = 14, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel1$residuals)

acf(ts(mymodel1$residuals),main = "ACF Residual")

pacf(ts(mymodel1$residuals),main = "PACF Residual")

#time series forecasting and average salary for both genders

myforecast1 <- forecast(mymodel1, level =c (95), h= 3*12)
plot(myforecast1,main = "Salary forecasting for the next 3years",xlab = "Time", ylab ="Average salary")

myforecast1[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 235                                                               
## 236 55624.57 46884.93 53731.15 49696.10 48435.46 46998.95 49289.00
## 237 48459.07 47592.97 48275.17 47868.02 47755.90 48104.21 49086.30
## 238 48602.55 48177.07 48510.69 48313.59 48253.43 48229.57 48411.94
##          Aug      Sep      Oct      Nov      Dec
## 235                                     46477.44
## 236 47347.17 49429.38 47061.29 49742.96 47532.72
## 237 48114.71 49144.91 48083.74 49384.94 48155.50
## 238 48244.26 48422.98 48230.09 48457.86

Male average salary

library(forecast)
library(readr)
library(zoo)
newsal <- drop_na(Sal)
msal <- subset(newsal, sex_name == "Male", select = c(avg_wage_ft, year,soc_name,sex_name))
head(msal)
msal$avg_wage_ft <- as.numeric(msal$avg_wage_ft)
msal$Date <- paste (msal$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata2 <- ts(msal$avg_wage_ft,frequency = 12)
tsdata2
##           Jan       Feb       Mar       Apr       May       Jun       Jul
## 1    86682.40  85707.40  85267.30  51929.70  49598.00  50787.10 178424.00
## 2    66271.40  70643.50  74757.40 103035.00 101524.00  95358.60  63128.90
## 3    70865.60  57713.90  57285.50  39245.60  42177.00  45607.10  65784.40
## 4    81777.00  84362.90  84184.80  50066.80  49178.10  50804.80 114071.00
## 5    35993.20  30974.90  56441.90  31216.40  33891.00  37167.80  43099.00
## 6    62677.00  64969.30  66329.80  80998.70  87303.10  88056.70 115709.00
## 7    58937.00  60610.60  55121.80 103141.00 101416.00 112492.00  68698.10
## 8    64332.90  79193.40 104016.00  22098.40  21858.30  21117.80  37987.60
## 9    35091.30  35086.40  35455.50  44099.10  40037.90  39530.40  25782.00
## 10   27590.80  28776.30  27830.30  26104.10  25778.40  25296.80  32622.80
## 11   39017.60  47352.10  46781.80  57650.40  55980.80  57691.00  36047.70
## 12   39408.50  46527.70  44432.00  41292.60  42417.60  43499.10  31244.30
## 13   32468.70  32301.00  70826.00  73451.00  74795.70  67236.50  40348.10
## 14   72404.30  83228.30  84241.30  31178.10  30338.30  31498.20  77211.20
## 15   33303.60  29171.00  31718.10  32338.50  31612.00  32333.10  29171.10
## 16   30289.30  28789.30  28835.50  34437.90  34740.20  34290.90 103558.00
## 17   49411.20  50732.00  49294.30  59287.10  58387.50  62070.00 147386.00
## 18   95376.10  92031.90  94479.60  90281.20  89507.80  93124.60  57772.80
## 19   50389.30  49619.10  50710.70  53101.60  50188.00  52099.30  30748.90
## 20   80767.10  63960.60  65244.80 109032.00 104883.00 105495.00  62873.40
## 21   90825.90  82935.60  85342.70  98374.30 100899.00 103552.00  42945.60
## 22   83858.90  85473.30  85178.40  42615.00  42966.10  42349.40  78912.60
## 23   80560.80  79440.50  79188.90  43224.20  43356.20  43244.00  70464.30
## 24   36532.00  37239.10  38719.20  28939.00  29296.40  29635.60  74143.00
## 25   39053.40  31596.60  30480.10  22975.20  22900.50  23543.40  34313.30
## 26   42698.80  41878.50  44373.70  38262.10  35025.50  39610.50  13036.90
## 27   51499.00  49107.10  49695.50  49514.10  48498.60  48604.00  66689.30
## 28   74121.90  77619.80  83428.20  23097.10  20668.30  20057.10  30990.90
## 29   25750.50  26901.10  28398.60  23847.80  35136.80  64308.40  32571.00
## 30   41768.90  41615.20  46837.50  72076.70  66366.70  74604.80 166196.00
## 31   57316.40  57447.30  56718.50  73800.00  73958.10  75544.30  55437.20
## 32   40319.90  41757.60  47655.60  17772.50  17978.70  18219.70  37704.50
## 33   47082.30  47294.80  48116.60  40090.40  42263.30  43334.10  37882.50
## 34   39829.40  42113.70  48268.80  78031.20  98212.30  98100.20  55378.70
## 35   39830.50  39104.50  42959.10  90671.60  89170.80  92463.00  49710.80
## 36   27235.20  26530.60  26230.40  40313.60  39379.80  40005.10  27360.30
## 37   47653.40  46796.30  46241.10  71574.80  73100.40  71160.30  49887.60
## 38   59773.80  59945.50  54194.90  50799.70  49596.70  50141.60  40254.20
## 39   43975.60  43741.50  41947.70  72987.60  72236.70  65563.50  69772.60
## 40   42936.80  41958.60  42585.70  35529.80  31803.80  29378.30  34691.70
## 41   36937.30  35146.90  33606.80  34355.90  32555.60  31251.20  90036.10
## 42   99077.80 103275.00 109630.00  69936.90  69457.40  67449.90  66083.00
## 43   52900.60  53914.80  56175.90  53013.20  53128.60  51918.80  36980.00
## 44   36164.10  36701.30  36763.30  63776.20  63217.30  65117.60  40087.40
## 45   57296.10  56253.90  57913.60  79554.70  81774.20  83199.10  55880.40
## 46   74759.80  73648.90  77621.50  59112.60  59724.30  58455.30  52263.00
## 47   48452.60  46843.30  44963.60  54521.70  55223.60  56766.00  34256.80
## 48   20754.70  19280.40  20546.20  19040.30  18842.30  18939.60  34903.90
## 49   54735.80  53199.50  54323.80  31276.20  31908.50  31894.00  34830.10
## 50   39988.10  33655.50  30924.80  29810.50  26356.70  23179.20  25031.60
## 51   45448.30  48682.00 100513.00  96896.20  96809.40  46683.60  53889.30
## 52   21893.80  24262.40  23692.60  23937.60  25323.00  38987.10  32569.10
## 53   32220.10  45623.00  42965.60  42954.20  44294.50  36086.30  36551.70
## 54   43090.60  44611.20  28537.10  28350.90  24146.50  25052.40  21211.70
## 55   31250.20  31553.50  37039.80  38743.40  36511.00  22856.10  20585.60
## 56   49016.40  49691.60  84656.60  83469.00  81530.90  64026.20  64505.00
## 57   73078.80  74917.30  77615.90  73415.40  70774.20  30047.20  30276.70
## 58   91059.80  96155.50  47166.60  47217.70  47446.70  30678.20  32521.10
## 59   75587.30  76563.50  72410.10  70333.40  70280.40  29988.20  32453.30
## 60   37665.80  41866.80  73596.50  86807.40  84098.70  28134.50  28860.60
## 61  155950.00 157104.00  50781.70  51502.50  50641.50  30015.50  26714.90
## 62   45522.20  44898.30  31855.70  31135.60  28482.00  59762.90  57690.60
## 63   61959.60  76301.40  70493.40  63955.50  65700.20  39542.90  37354.70
## 64   28854.50  29221.80  32541.20  32866.80  34463.50  40927.20  40884.50
## 65   33030.30  33880.70  38566.60  37793.90  38656.90  38993.60  36626.60
## 66   89259.90  92833.80  77179.00  78660.90  81498.30 103208.00 104354.00
## 67   97187.20  93234.30  74851.40  75620.60  77534.20 104462.00 101133.00
## 68   43039.20  41204.90 101621.00  97453.20  96032.80  24988.60  19433.60
## 69   51712.60  59460.10  31558.20  27825.00  31823.10  35634.70  37132.40
## 70   81469.20  83952.80  47326.70  47874.90  48625.50  50789.10  51647.80
## 71   20473.60  21062.50  31014.20  29276.40  29120.00  48432.20  49030.40
## 72   87971.40  92511.00  26292.30  26624.00  26404.60  42957.10  45264.90
## 73   63403.40  61425.30  36704.20  36022.70  36500.00  36111.60  32005.50
## 74   54846.30  59168.10  91635.20  91495.50  91472.40  42395.80  41401.30
## 75   42578.30  43462.90  33617.30  31877.40  31537.70  44984.30  43175.40
## 76   44860.50  46920.40  34435.20  34004.50  34996.10  75530.70  78231.30
## 77   41335.30  40242.60  28472.20  29621.10  29649.00  29790.90  28436.90
## 78   45259.10  54121.50  30421.30  28029.20  28415.00  52387.70  54299.70
## 79   23228.00  26360.50  44992.70  49459.80  55744.70  98111.70  85345.00
## 80   50885.80  56186.40  68698.50  68612.10  74978.00  26936.30  26640.70
## 81  134541.00 122741.00  36492.90  36172.70  36820.30  67022.70  68089.80
## 82   36803.90  35915.50  31951.00  30930.70  31459.20  77942.20  77874.20
## 83  104093.00 123099.00  65982.40  66450.10  67952.20  55466.40  55862.00
## 84   97298.20  90578.60  59398.40  61327.30  62785.20  69147.70  70347.40
## 85   52640.70  52277.40  58631.90  53169.20  49784.70  29690.40  34477.80
## 86   27548.70  28446.00  36489.20  38251.90  40868.30  40579.00  39459.70
## 87   22320.70  21408.60  40163.70  39559.10  36153.20  29724.30  26996.20
## 88   21924.20  23476.50  30084.70  28021.10  27602.10 133563.00 135691.00
## 89  106177.00 124726.00 118203.00 116035.00 115308.00  35139.20  39391.90
## 90   40296.60  45581.90  32427.90  36552.40  41630.80  42832.10  44836.30
## 91   96980.20 107782.00 229096.00 233337.00 240996.00  37303.40  37069.90
## 92   79683.20  87999.50  60370.80  60370.00  61429.70  56585.60  56598.80
## 93   52683.00  52961.20  78722.70  77208.00  80834.70  80505.70  81886.80
## 94   37468.80  37028.60  19187.00  20829.70  24644.20  33520.60  30893.00
## 95   32036.70  33117.80  63222.60  62588.50  63386.00  42105.20  40207.20
## 96   67181.20  67480.70  56995.30  57334.80  62627.60  37559.50  34060.50
## 97   85346.90  95234.00  95693.80  98623.80 108372.00  84312.70  83414.40
## 98   55049.30  55830.10  91862.10  96020.30  93227.90  86599.80  74588.30
## 99   57822.20  65632.70  34663.90  39032.10  45463.70  71812.30  74868.30
## 100  41083.80  38117.20 119092.00  55356.20  56290.50  28439.30  28670.00
## 101  40150.20  39751.00  56332.10  56558.80  45031.70  23531.50  23705.00
## 102  43959.60  44295.60  45572.90  46700.70  36016.70  59686.80  64990.90
## 103  59071.10  55881.80 103488.00  91961.20  99695.50  70888.10  72825.90
## 104  28672.10  26326.00  48520.70  48194.00  48187.70  40494.50  39481.60
## 105  39486.10  41528.60  30964.80  30144.50  30058.60  20040.90  21878.40
## 106  72954.70  73596.70  31404.90  30397.90  30310.70  32737.80  34691.00
## 107  78920.50  73701.60  38050.70  40196.50  40323.10  47064.40  46718.30
## 108  37094.80  38312.20  74263.20  67482.10  62392.30  44549.40  43587.90
## 109  26849.70  26650.40  33908.00  33533.00  35975.00  31813.00  35351.20
## 110  53066.00  51574.20  40437.10  39532.40  41688.20  51848.60  53466.00
## 111  32327.80  29358.80  73259.60  70966.00  67876.00  59420.40  61474.40
## 112  31043.60  31319.90  62947.20  59380.90  70225.90  42419.80  42027.80
## 113  36322.80  37546.60  41355.00  42855.90  44779.30  28740.70  27196.70
## 114  61512.90  62290.20   2002.53   2027.83  49199.10  41721.80 105603.00
## 115  48650.90  83414.50  77271.40  71697.00  57278.90  56145.60  59369.60
## 116  66103.40  42773.80  46422.40  44270.00  53860.50  51525.70  51822.60
## 117  29424.50  77743.00  77313.90  77985.00  18880.60  20527.40  24554.60
## 118  27583.10  25065.70  25331.60  26534.10  47119.10  46707.10  47588.50
## 119  36839.20  37276.80  36515.10  35989.90  51464.50  52686.40  50023.60
## 120  34160.10  65888.30  67087.00  72430.40                              
##           Aug       Sep       Oct       Nov       Dec
## 1   175534.00 177805.00  48778.20  31693.80  35615.50
## 2    59704.90  63631.80  92254.20  91961.30  90847.90
## 3    70146.20  72558.50  46509.80  46152.10  49015.60
## 4   117501.00 121559.00  39549.50  38154.10  45188.80
## 5    35946.40  43311.30  84891.40  74257.20  78098.90
## 6   120292.00 122867.00  57660.30  60639.70  69569.70
## 7    68460.00  73682.90  86373.20  81579.80 100890.00
## 8    39227.20  40428.60  31610.70  33793.10  37375.50
## 9    28018.50  29367.60  43058.00  42790.00  43574.00
## 10   32814.80  34589.20  34664.70  34310.50  35595.50
## 11   36835.40  45596.10  82364.60  81726.20  84542.20
## 12   31776.50  32202.20  47421.00  48296.30  48689.80
## 13   42018.60  43191.60  31949.10  30396.10  30816.10
## 14   52029.70  56159.50  32350.60  31872.40  33266.50
## 15   31924.30  31689.90  27833.70  28314.80  28807.70
## 16   91860.60  88303.60  46751.60  47027.00  46231.00
## 17  153295.00 158343.00  22086.00  21086.80  21237.60
## 18   57149.90  57118.50  25126.90  24283.20  25006.70
## 19   29469.50  32683.90  20940.80  22047.40  22863.50
## 20   71001.50  57706.10  72414.50  70993.60  67459.30
## 21   43810.60  45403.70  78799.30  76532.70  81124.70
## 22   79447.50  80712.00  53489.30  53279.70  53781.80
## 23   77265.80  71734.70  54163.90  52371.40  53941.70
## 24   73275.00  73006.50  42454.40  45848.90  46418.60
## 25   35388.20  37202.00  67338.40  66080.70  63924.40
## 26   14865.40  18436.60  42302.20  41039.40  39008.10
## 27   67387.50  67797.20  59713.70  63189.80  67369.30
## 28   31718.40  33053.30  37846.10  37521.30  37069.30
## 29   34649.50  33951.90  88119.10  85288.50  83640.60
## 30  181351.00 188445.00  57847.30  71230.20  73794.60
## 31   53795.50  58516.00  47055.80  46525.70  50687.60
## 32   37518.10  39116.90  33005.80  36575.80  41351.50
## 33   38292.40  38937.50  24943.20  25056.90  25056.70
## 34   49621.80  48605.60  81918.30  79195.10  80288.80
## 35   47521.10  48570.60  58145.50  58681.00  62440.50
## 36   26470.20  24371.50  38832.00  38817.70  38870.10
## 37   50174.70  52365.10  27644.00  28776.20  30795.10
## 38   36792.10  39203.80  57192.40  57424.50  56850.10
## 39   71148.20  68058.00  35305.30  34485.90  30326.40
## 40   31971.60  29666.00  60794.50  60848.80  67352.20
## 41   86025.70  87692.30 169056.00 162353.00 186121.00
## 42   66509.70  68395.50  67142.20  65911.90  66242.30
## 43   38313.50  40700.70  90773.00  87517.40  88172.00
## 44   39635.20  40276.60  37238.70  39369.60  40531.60
## 45   56741.20  58236.60  54065.90  50821.20  51803.10
## 46   53814.40  53063.00  56839.40  55665.90  55471.40
## 47   26890.50  21879.70  27846.00  26253.50  27351.20
## 48   35881.10  37091.70  25829.10  26847.90  28193.80
## 49   34922.00  26400.80  85468.60  71721.70  69822.40
## 50   25347.90 103049.00  98352.80  99102.10  47039.10
## 51   61653.30  32646.40  30557.10  30640.70  23184.30
## 52   33331.30  27506.60  29128.70  33769.50  32220.40
## 53   35938.10  38649.80  38888.30  39507.50  42318.10
## 54   21782.20  22673.50  22217.90  21789.70  31694.70
## 55   20831.50  21596.60  21610.40  22104.10  45515.70
## 56   67854.70  45245.40  45371.00  45688.70  74577.40
## 57   31325.20  41171.50  42398.70  43037.70  79669.50
## 58   29577.70  45330.60  45412.00  44340.80  78185.10
## 59   37544.50  26188.60  25822.20  26765.50  37854.20
## 60   29274.40  27608.40  28021.10  31221.20 153323.00
## 61   28766.20  45321.40  40038.30  38624.00  45885.30
## 62   51806.20  38250.30  39712.80  40619.60  64225.50
## 63   38919.20  60730.00  59501.80  59496.30  38234.00
## 64   43870.00  20660.20  20452.40  22551.70  32545.30
## 65   38812.30  93881.10  93540.20  94058.70  91288.90
## 66  103835.00  36401.80  36205.50  38226.30  87316.90
## 67   99247.20  41110.60  38958.30  37198.30  45423.20
## 68   41121.20  40054.00  38775.20  38665.00  52041.60
## 69   39567.50  28745.10  30590.40  32243.60  82091.10
## 70   56131.20  35222.80  37073.30  41991.60  19745.40
## 71   55409.90  30657.20  32449.40  34469.40  88146.70
## 72   49830.20  20867.50  20181.90  21097.00  65168.70
## 73   40704.40  63497.80  64617.30  63938.70  54519.20
## 74   36636.20  80534.10  74850.20  65840.80  44462.10
## 75   44185.30  22929.10  22955.90  23829.80  45642.40
## 76   71518.10  19060.30  24793.60  26407.50  41848.00
## 77   28301.80  39003.80  39860.30  41121.60  44526.30
## 78   54219.60  25140.20  26301.90  23602.50  23047.20
## 79   91465.80  66088.00  66200.80  68412.80  46663.70
## 80   26502.80 186196.00 176829.00 161803.00 126986.00
## 81   72910.40  34899.90  30870.30  49627.70  36772.50
## 82   77105.30  51009.40  49866.90  51946.50 115425.00
## 83   51949.50  72590.90  70595.50  73104.80  86151.40
## 84   79300.90  65670.70  66565.20  67197.40  52563.90
## 85   35650.80  21187.40  21998.00  21967.70  28496.40
## 86   42461.20  73812.80  69092.80  70155.00  23523.10
## 87   28491.20  46889.90  48855.70  49591.00  23912.60
## 88  139495.00  36413.90  35857.50  36431.50 156850.00
## 89   31503.80  39295.70  33565.40  35422.70  37329.40
## 90   47819.70  83125.70  78467.90  78976.30  97438.00
## 91   37348.30  27210.20  27138.10  32743.40  78498.10
## 92   54972.40  57133.60  55722.10  55623.30  54214.10
## 93   85486.20  50765.00  52479.30  52415.20  39673.10
## 94   27293.90  33334.90  32688.30  33803.90  31798.80
## 95   39376.30  61966.20  65856.30  68329.80  62990.40
## 96   35804.70  78612.70  79930.90  79201.50  85656.30
## 97   95578.30  70485.30  50791.40  48689.70  56154.40
## 98   78735.20  50813.40  49845.40  50231.10  59584.50
## 99   76526.20  31560.90  32816.10  33120.40  40437.40
## 100  30686.50  70718.10  68353.40  70181.70  40862.80
## 101  25651.80  71470.50  71054.80  71435.20  44552.70
## 102  49089.50  28454.30  29237.90  29358.10  52557.60
## 103  75435.70  77015.90  77255.00  79527.00  26417.60
## 104  40109.80  92986.90  92891.80 102640.00  40517.60
## 105  22690.50  35372.90  36388.80  37546.60  76635.90
## 106  30456.70  35646.20  33336.90  35445.30  81716.10
## 107  44846.90  86405.50  84648.40  86606.00  40384.20
## 108  45325.00  50804.30  47785.90  47750.30  27514.90
## 109  34929.50  37961.80  27909.20  26791.60  51411.30
## 110  54422.70  39850.60  48039.20  37740.50  29322.70
## 111  59139.90  30658.40  30909.20  30817.40  31692.40
## 112  44274.20  26811.10  28481.20  25263.60  34543.80
## 113  27462.10  39789.20  43560.80  42434.70  61442.20
## 114  56308.40  52655.60  53641.00  38502.30  50883.30
## 115  31673.50  28607.40  27963.20  53991.40  56756.50
## 116  43913.30  48615.50  44032.10  31509.90  28937.80
## 117 131111.00 126741.00 121776.00  26353.70  23047.80
## 118  64753.80  59487.30  57384.80  35983.70  36495.40
## 119  24908.80  24751.20  27287.70  34129.40  34563.40
## 120
ddata2 <- decompose(tsdata2)
plot(ddata2)

plot(ddata2$seasonal)

plot(ddata2$trend)

#actual plot 
plot(msal$avg_wage_ft,type ="l",main = " Male Average salary",xlab= "Year",ylab="Average Salary")

#class

class(tsdata2)
## [1] "ts"

arima model

mymodel2 <- auto.arima(tsdata2)
mymodel2
## Series: tsdata2 
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1      ma2      ma3     sar1     sar2    sma1    sma2
##       0.8987  -0.0612  -0.0062  -0.5274  -1.1454  -0.4460  1.1298  0.4303
## s.e.  0.0387   0.0486   0.0429   0.0382   0.3098   0.3749  0.3222  0.3837
##           mean
##       53491.76
## s.e.   1885.79
## 
## sigma^2 estimated as 3.3e+08:  log likelihood=-16072.17
## AIC=32164.34   AICc=32164.5   BIC=32217.01

fitting the best model

auto.arima(tsdata2,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 32129.98
##  ARIMA(0,0,0)            with non-zero mean : 33374.1
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 32356.22
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 32761.37
##  ARIMA(0,0,0)            with zero mean     : 35583.8
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 32174.49
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 32129.05
##  ARIMA(2,0,2)            with non-zero mean : 32172.54
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 32132.99
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 32134.92
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 32129.81
##  ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 32335.56
##  ARIMA(3,0,2)(1,0,0)[12] with non-zero mean : 32130.73
##  ARIMA(2,0,3)(1,0,0)[12] with non-zero mean : 32114.83
##  ARIMA(2,0,3)            with non-zero mean : 32155.33
##  ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 32121.28
##  ARIMA(2,0,3)(1,0,1)[12] with non-zero mean : 32114.6
##  ARIMA(2,0,3)(0,0,1)[12] with non-zero mean : 32157.22
##  ARIMA(2,0,3)(2,0,1)[12] with non-zero mean : 32123.09
##  ARIMA(2,0,3)(1,0,2)[12] with non-zero mean : 32115.58
##  ARIMA(2,0,3)(0,0,2)[12] with non-zero mean : 32157.79
##  ARIMA(2,0,3)(2,0,2)[12] with non-zero mean : 32112.21
##  ARIMA(1,0,3)(2,0,2)[12] with non-zero mean : 32108.97
##  ARIMA(1,0,3)(1,0,2)[12] with non-zero mean : 32112.99
##  ARIMA(1,0,3)(2,0,1)[12] with non-zero mean : 32120.39
##  ARIMA(1,0,3)(1,0,1)[12] with non-zero mean : 32112.03
##  ARIMA(0,0,3)(2,0,2)[12] with non-zero mean : 32135.89
##  ARIMA(1,0,2)(2,0,2)[12] with non-zero mean : 32137.78
##  ARIMA(1,0,4)(2,0,2)[12] with non-zero mean : 32110.89
##  ARIMA(0,0,2)(2,0,2)[12] with non-zero mean : 32141.84
##  ARIMA(0,0,4)(2,0,2)[12] with non-zero mean : 32134.75
##  ARIMA(2,0,2)(2,0,2)[12] with non-zero mean : 32136.89
##  ARIMA(2,0,4)(2,0,2)[12] with non-zero mean : 32121.52
##  ARIMA(1,0,3)(2,0,2)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,3)(2,0,2)[12] with non-zero mean : 32164.34
## 
##  Best model: ARIMA(1,0,3)(2,0,2)[12] with non-zero mean
## Series: tsdata2 
## ARIMA(1,0,3)(2,0,2)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1      ma2      ma3     sar1     sar2    sma1    sma2
##       0.8987  -0.0612  -0.0062  -0.5274  -1.1454  -0.4460  1.1298  0.4303
## s.e.  0.0387   0.0486   0.0429   0.0382   0.3098   0.3749  0.3222  0.3837
##           mean
##       53491.76
## s.e.   1885.79
## 
## sigma^2 estimated as 3.3e+08:  log likelihood=-16072.17
## AIC=32164.34   AICc=32164.5   BIC=32217.01

Residuals

library(tseries)
adf.test(mymodel2$residuals)
## Warning in adf.test(mymodel2$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel2$residuals
## Dickey-Fuller = -10.304, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel2$residuals)

acf(ts(mymodel2$residuals),main = "ACF Residual")

pacf(ts(mymodel2$residuals),main = "PACF Residual")

#forecasting male salary and mean

myforecast2 <- forecast(mymodel2, level =c (95), h= 3*12)
plot(myforecast2,main = " Male Salary forecasting for the next 3years",xlab = "Time", ylab ="Average salary")

myforecast2[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 120                                     56204.39 55175.57 52489.47
## 121 53455.69 52496.59 52492.91 52450.65 53340.98 53364.44 53472.37
## 122 53269.69 53710.18 53697.72 53709.55 53298.04 53323.22 53322.71
## 123 53425.87 53383.15 53429.67 53462.48                           
##          Aug      Sep      Oct      Nov      Dec
## 120 53530.29 53556.97 53587.43 53300.74 53333.17
## 121 52856.12 52902.87 52919.99 53277.89 53313.30
## 122 53629.00 53621.66 53640.67 53405.52 53392.66
## 123

Female average salary

library(forecast)
library(readr)
library(zoo)
newsal <- drop_na(Sal)
fsal <- subset(newsal, sex_name == "Female", select = c(avg_wage_ft, year,soc_name,sex_name))
head(fsal)
fsal$avg_wage_ft <- as.numeric(fsal$avg_wage_ft)
fsal$Date <- paste (fsal$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata3 <- ts(fsal$avg_wage_ft,frequency = 12)
tsdata3
##           Jan       Feb       Mar       Apr       May       Jun       Jul
## 1    55458.50  54431.80  55109.50  18393.40  18262.40  17693.80  99920.40
## 2    58112.50  56859.20  56820.50  60364.50  56464.60  58797.30  53526.10
## 3    43737.20  44096.90  46257.90  33057.80  40732.10  37884.60  42522.10
## 4    79105.70  73835.60  75047.70  40489.90  28436.40  25723.20  53670.20
## 5    21655.00  20995.30  24421.60  33578.50  31982.60  30460.30  27280.20
## 6    48093.40  47688.70  49037.10  52731.10  50930.70  54658.70 108388.00
## 7    39494.80  36048.70  32984.60  66644.30  66917.60  76335.20  40377.10
## 8    73956.70  75341.10  74997.10  24155.60  22663.70  28080.20  27475.10
## 9    34173.20  36675.70  35999.00  27276.60  28303.80  28284.00  40103.30
## 10   18904.00  20467.70  19732.10  26011.40  25877.50  28655.30  33819.20
## 11   53531.90  52171.90  48746.10  44524.00  48361.30  42067.60 117369.00
## 12   15019.00  15208.70  48312.50  45628.70  45393.80  52017.90  49409.40
## 13   31873.40  33441.30  27245.70  26452.10  26328.70  47082.80  48517.50
## 14   54614.50  73333.40  51424.40  20025.30  19912.70  35392.40  38086.00
## 15   33965.80  23803.30  21265.40  21384.80  21870.20  31249.60  31285.60
## 16   81590.00  87485.30  29178.60  23489.80  27868.60  47186.70  43281.10
## 17  112080.00 117377.00  19860.30  19992.10  20871.90  50021.20  48687.00
## 18   49642.10  51175.10  21139.80  21922.60  21552.70  40946.90  38707.40
## 19   42596.20  42046.80  19525.40  19315.40  19958.50  59180.90  35994.70
## 20   48432.30  49956.10  56283.50  56704.30  58544.10  54070.70  73954.60
## 21   32147.60  32373.60  56444.90  60543.50  66690.70  86707.50  85720.50
## 22   68086.00  72914.80  48508.50  48602.60  51058.20  68051.40  67947.20
## 23   46249.70  45416.00  41998.40  40154.90  39674.30  27985.80  36569.90
## 24   60732.90  58532.10  46767.50  46138.00  41185.20  35397.80  35651.30
## 25   30127.70  31161.50  39913.10  41775.90  49642.30  41559.30  42222.90
## 26   16789.30  17244.80  30263.10  28110.70  31344.10  33183.40  33157.60
## 27   43647.20  41969.20  56849.20  53925.50  57143.10  22600.30  21981.20
## 28   30157.30  30522.90  24578.60  24211.20  20018.80  26718.90  26559.50
## 29   66664.10  68362.70  29634.10  30393.20  30670.50  47217.00  48606.80
## 30   45949.00  45763.60  58566.70  59261.60  59506.10  50092.90  49924.20
## 31   39524.30  37177.00  14482.40  14134.80  15217.70  32227.90  32520.70
## 32   38483.70  39766.30  31235.20  30696.30  30225.60  31577.30  19806.60
## 33   79033.30  64595.20  56348.00  49747.90  47221.80  47054.30  59811.90
## 34   82476.30  83972.40  80268.20  51710.40  51334.00  44163.60  27575.80
## 35   35365.50  25923.30  25757.10  22528.80  22554.70  22839.70  30600.90
## 36   85748.90  50835.30  19568.60  38347.40  38800.70  37505.10  33733.50
## 37   42442.70  42512.50  39710.60  26259.30  28212.50  44065.80  43773.70
## 38   62123.40  62268.50  49200.30  49381.10  49982.70  25729.40  31905.50
## 39   18307.40  18242.10  24016.40  24047.70  26768.80  54454.00  57619.60
## 40   64880.00  64752.50  67484.60  54697.70  51167.50  54359.70  65889.90
## 41   67162.80  59464.80  57604.50  66211.20  62673.80  61867.60  51786.70
## 42   26136.10  25005.00  25811.70  92230.70  77126.70  74819.70  27225.70
## 43   30190.70  29806.40  29122.90  37257.90  36209.70  37008.70  51983.90
## 44   43367.20  43368.90  44344.10  38387.30  37200.80  36492.30  65271.00
## 45   46268.70  41265.30  41293.20  38397.30  38660.00  39234.30  20321.20
## 46   29103.40  30314.20  27900.80  26007.00  26280.20  25795.00  17042.60
## 47   28827.40  25594.80  28154.70  21310.20  20543.70  21533.00  38503.10
## 48   53126.70  51363.70  55342.50  25930.80  32272.30  42464.00  43468.70
## 49   43466.10  46518.20  49366.60  45600.20  42968.60  40492.00  69208.90
## 50   26796.50  26827.30  14648.40  15195.00  15228.60  21971.70  21490.90
## 51   26470.00  24553.30  44747.90  49387.40  48682.70  32729.20  32943.40
## 52   45542.00  42869.50  11059.60  11072.30  11212.20  21882.70  12015.20
## 53   19264.00  22804.30  34176.10  34742.00  32064.20  13905.60  10426.90
## 54   21768.00  21547.10  33151.40  33938.50  38018.60  70888.00  73787.30
## 55   36982.80  36792.70  57046.00  53723.70  65013.40  62761.20  62109.00
## 56   35787.20  35529.60  65044.00  65894.00  74213.20  31753.60  32532.30
## 57   34415.00  34691.00  50347.80  49188.90  48641.80  51192.40  53159.90
## 58   21009.70  21498.50  38563.20  35604.70  35665.80  43280.60  45208.90
## 59   19352.10  18335.70 102656.00 103134.00 107421.00  45005.90  45362.30
## 60   33014.30  31277.70  40209.10  39084.60  38954.80  25939.30  24467.50
## 61   25725.70  25826.40  47592.00  46210.10  46588.00  47614.20  47846.50
## 62   42398.20  21068.90  18595.70  19171.30  24029.20  35036.60  34086.10
## 63   19903.70  25548.10  26980.90  27590.40  34697.00  35075.00  40466.50
## 64   88881.90 105870.00  84971.80  56030.80  56325.10  60990.10  69021.30
## 65  115731.00  91785.80  76672.80  59849.00  55267.50  61637.50  69468.30
## 66   35795.40  35841.00  36879.40  64251.40  62439.30  67023.60  33218.60
## 67   44686.80  42973.60  43950.50  36304.40  35606.40  35915.40  19143.70
## 68   46022.90  43451.10  44346.90  14622.20  14961.90  15210.90  64311.70
## 69   23806.50  24378.70  24877.40  40529.50  40698.60  43037.80  25475.30
## 70   25873.70  26792.90  23814.00 108332.00 108457.00 109827.00  18816.30
## 71   31979.10  34270.80  34602.70  36063.00  36141.60  44883.90  45434.70
## 72   65138.90  66296.50  67523.00  26827.70  22778.00  24180.00  71072.40
## 73   23796.50  22617.60  21524.70  36175.00  35412.70  36235.20  20882.30
## 74   23592.40  25132.30  25466.30  54216.80  57300.50  53880.40  23382.60
## 75   19964.40  18098.20  20473.70  32108.50  32213.90  40660.90  36524.70
## 76   23240.80  17214.50  11430.40  37002.30  37192.20  43438.60  29193.90
## 77   38533.50  33103.50  33018.60 102568.00 101909.00  90391.60  60592.20
## 78   54529.60  68846.80  75470.70  25201.50  25433.10  25754.90 116304.00
## 79   27215.50  26348.50  26109.90  66030.60  64068.10  65149.90  52043.90
## 80   30929.00  31230.30  31535.40  58829.80  58854.00  60918.40  42967.90
## 81   52846.40  56571.80  56698.90  48165.70  49668.00  48650.40  43571.10
## 82   41945.00  46451.90  53564.80  58427.60  59765.20  61420.50  43932.00
## 83   46911.60  45984.10  49532.10  23316.50  22575.70  23059.60  19334.30
## 84   20755.20  22504.50  22767.10  20898.10  30135.30  31914.70  46733.50
## 85   26332.70  25787.40  29351.40  19557.10  10095.80  10223.40  38373.70
## 86   24017.50  24225.70  24241.90  78925.60  75475.80  80247.40  37481.80
## 87   98192.20  96341.90  98169.70  30991.60  30222.80  32136.70  28506.40
## 88   23175.20  25156.10  28633.30  42423.20  44909.50  45739.20  65298.20
## 89  159392.00 162641.00 165391.00  31992.00  31839.80  36043.30  41738.20
## 90   51625.50  52904.50  53600.10  53477.30  53574.50  53449.80  49772.90
## 91   57749.50  57601.80  57953.70  42102.90  35338.10  31894.90  39546.70
## 92   23639.90  23872.50  23939.00  18790.20  17723.20  18480.90  21967.30
## 93   47434.80  49620.90  47950.30  41401.90  40895.10  41663.20  62445.80
## 94   37115.20  36939.00  38090.30  31388.90  26236.00  32608.80  49455.20
## 95   63065.10  77160.00  74678.80  52935.40  54219.80  61742.80  26323.50
## 96   65232.60  62492.90  63073.60  71460.60  67648.80  72656.80  48270.10
## 97   20773.60  20797.50  21060.20  51520.40  53331.20  57948.00  25755.90
## 98   43350.40  36886.30  39175.90  30957.00  24520.70  26139.10  61454.90
## 99   34404.40  35392.40  37129.20  19448.90  21644.50  19027.20  52686.30
## 100  26621.90  23380.90  24874.40  36559.60  42329.90  39826.40  36867.80
## 101  49126.00  50587.70  53688.10  62703.80  63910.70  66644.70  27604.60
## 102  35292.00  34937.20  35294.40  66471.30  69643.00  72220.40  47392.30
## 103  20890.00  21389.60  22031.60  18685.60  23297.70  25503.60  73089.20
## 104   8337.42  19315.70  24182.10  10075.90  56913.10  55689.10  56493.40
## 105  42655.20  73418.10  70718.10  71368.60  43021.80  41589.80  41181.60
## 106  39999.30  33971.70  35268.20  36676.30  27640.20  26992.10  25825.50
## 107  44506.10  44249.80  44316.00  38018.60  42230.80  40611.80  44978.80
## 108  27354.60  28720.00  29191.10  42897.90  45091.60  40793.10  36347.70
## 109  20973.70  21498.60  22504.20  51975.20  49255.70  53811.40  46056.40
## 110  26495.00  25808.60  25231.50  33252.40  36906.30  32586.00  27694.10
## 111  32824.10  31240.20  27320.10  26247.00  66966.40  66335.10  67948.40
## 112  24000.20  96525.50  94786.60  85413.40  35360.50  30450.00  37793.90
## 113  37058.50  28033.70  25370.70  28884.60  65047.80  64813.20  61534.40
## 114  82072.90  27128.60  25399.10  23029.10  21497.60  21669.90  22742.30
## 115  47105.20  29327.90  27159.60  28285.40  22150.80  24043.70  25469.70
## 116  20763.40  32879.60  33181.10  34022.40  46394.70  45873.90  45781.60
##           Aug       Sep       Oct       Nov       Dec
## 1   127523.00 128534.00  31223.40  33370.20  36439.60
## 2    54962.60  55028.40  83664.60  78976.00  84087.90
## 3    45328.70  46605.20  33219.20  36176.50  35251.20
## 4    54758.00  71981.80  65598.40  43861.10  44481.30
## 5    30139.00  31073.30  31448.70  33105.90  48879.90
## 6   109712.00 105819.00  45350.90  46966.60  38996.70
## 7    37365.50  37426.70  68732.10  50789.70  50651.90
## 8    27695.60  28458.80  32432.10  33178.20  45162.80
## 9    39944.20  39492.30  24091.30  23443.40  24135.80
## 10   34418.10  35284.10  31778.70  32064.00  32984.30
## 11  198142.00 200646.00  36745.80  36745.20  37645.00
## 12   51008.60  65350.60  79621.50  73783.00  28905.00
## 13   51215.50  23718.40  23831.40  24716.00  37122.20
## 14   36835.10  32947.30  31594.00  30774.80  20562.00
## 15   28829.30  29168.90  28188.00  30208.60  79828.50
## 16   43921.10  71998.50  69198.40  69581.20 105175.00
## 17   47785.50  68442.60  69943.50  71937.80  50403.00
## 18   37939.10  46136.60  46479.80  46874.10  21996.70
## 19   30185.40  72441.00  71827.30  71994.90  46794.00
## 20  132464.00  89686.10  88448.00  86841.20  39206.40
## 21   84761.70  33633.50  33239.50  34427.80  68039.40
## 22   66647.10  42875.40  38836.20  36705.70  47365.60
## 23   37206.30  27723.70  29854.10  33108.80  64519.40
## 24   31575.30  21261.80  20753.90  21132.00  29929.10
## 25   42147.90  30875.60  31284.10  31681.70  17206.60
## 26   33298.90  63359.80  60697.00  58633.50  43191.80
## 27   23820.50  27276.50  27416.80  28150.10  30404.70
## 28   26253.10  30481.80  29974.60  30044.90  63856.70
## 29   52128.30 145786.00 153984.00 157053.00  45177.80
## 30   50093.10  40905.50  43854.50  44429.00  41974.20
## 31   32828.80  24027.70  24394.70  28566.10  37826.90
## 32   18501.80  18463.50  11092.70  11105.40  11245.80
## 33   59807.80  59015.80  56298.70  41058.50  29181.80
## 34   27722.20  47435.90  23891.90  24711.40  26093.60
## 35   26359.70  26630.10  44033.70  43388.80  43366.10
## 36   32303.80  32626.20  48646.10  54293.70  42538.50
## 37   44747.40  38568.30  35362.00  32332.50  64518.90
## 38   30293.60  33400.80  20427.00  41465.30  18837.30
## 39   52386.20  24383.60  31660.20  31367.40  30636.90
## 40   68109.60  69704.80  57784.00  63806.10  68128.30
## 41   50318.10  46779.70  53132.30  52889.40  57329.20
## 42   25615.40  25918.40  53675.70  54251.90  62297.70
## 43   53697.60  55863.70  58332.70  57048.90  59867.40
## 44   63217.50  63771.40  47183.40  45294.90  40453.00
## 45   19867.40  21056.60  41736.90  43956.70  46272.70
## 46   20981.50  18703.40  19339.10  19964.20  19860.10
## 47   39001.30  39882.20  12645.00  11841.20  11962.40
## 48   44579.60  21120.60  27416.50  27959.90  26786.60
## 49   67159.50  66642.50  78752.80  67762.30  71304.50
## 50   20983.10  24914.10  25246.60  25257.80  27304.00
## 51   33363.20  24919.40  25367.50  25180.40  38381.50
## 52   12167.00  21338.00  20843.00  21109.00  19555.10
## 53   10608.90  18819.20  17686.20  17273.50  21427.30
## 54   76079.90  53471.80  53608.90  54765.30  33768.80
## 55   62595.40  32928.00  32840.30  33164.20  37225.90
## 56   32427.10  30521.20  31594.80  31214.70  35159.90
## 57   54865.70  30031.90  31265.30  31616.40  20464.80
## 58   42926.90  22926.00  23363.00  22743.40  19565.10
## 59   45901.80  24906.50  24255.60  25049.00  30919.30
## 60   24596.10  38859.10  38269.20  40761.10  25840.20
## 61   46614.70  10012.60  10139.20  40410.30  39999.20
## 62   24326.40  23842.90  27172.40  19319.90  19528.80
## 63   43946.30  43996.90  69354.70  68956.50  68223.50
## 64   69231.70  71384.60  28728.10  30565.80  30637.10
## 65   70796.10  71221.40  27118.70  27429.00  28153.00
## 66   31268.60  32308.20  32582.30  31183.30  33116.70
## 67   23097.40  24249.20  68566.70  77740.30  61215.70
## 68  129541.00 125226.00  16481.60  16812.80  16787.90
## 69   26835.80  26563.60  78392.40  82557.00  84283.80
## 70   19663.70  19730.20  42707.20  43057.90  42494.10
## 71   45642.70  45586.40  39725.50  41078.30  43935.80
## 72   65314.90  64494.40  39492.60  36278.70  34941.90
## 73   21786.30  22704.30  28922.10  30629.70  31036.10
## 74   19884.80  19480.00  43489.70  40485.80  35150.60
## 75   35615.70  13369.50  31334.50  31960.50  31211.30
## 76   20191.70  20446.80  19361.40  15882.00  15148.30
## 77   61850.60  65731.90  39191.10  37757.80  38211.30
## 78  116161.00 115829.00  96544.30  93605.40  92550.60
## 79   47376.70  48756.60  31493.70  31399.10  31432.20
## 80   43760.20  42158.40  86990.80  87685.40  88307.20
## 81   41979.60  42307.60  54842.60  55151.60  57247.60
## 82   43357.10  45490.60  39746.20  40028.20  40443.70
## 83   20872.40  20987.90  27289.40  32750.90  35674.30
## 84   47007.40  46877.40  18473.10  22007.00  33405.20
## 85   37999.60  38317.80  21763.70  24662.00  23108.70
## 86   30981.80  32819.30 122884.00 123026.00 124580.00
## 87   28944.50  29575.10  30794.30  30791.90  30074.40
## 88   64321.00  66105.20  62565.90  65447.20  73786.60
## 89   40317.00  30624.90  71371.50  75548.70  88551.30
## 90   49603.80  50585.70  47079.40  48046.00  48528.40
## 91   36751.30  36309.40  26414.70  26455.80  25685.40
## 92   22415.50  23524.00  24412.10  22233.20  22585.50
## 93   60724.50  64098.00  56998.00  61947.30  62831.00
## 94   49745.70  50495.30  60043.40  59518.80  60540.80
## 95   25911.80  30745.40  46430.30  46531.80  47564.90
## 96   43430.30  52244.40  59055.00  56869.60  54692.90
## 97   25753.30  25808.10  28741.30  30894.70  30298.10
## 98   60501.50  60642.90  30114.50  33036.20  34005.20
## 99   52162.30  53350.60  31410.50  30901.20  30758.80
## 100  34404.40  46513.90 189149.00 118336.00 112827.00
## 101  16322.10  13392.30  46230.60  45617.50  46265.60
## 102  43807.30  41330.30  29153.50  29424.70  29677.10
## 103  63339.90  47373.40  28652.40  27843.20  27690.10
## 104  34210.80  35481.30  38829.90  42280.20  42157.40
## 105  60677.80  61860.70  60623.30  48823.60  40484.70
## 106  20025.30  20278.30  35841.30  35882.50  36335.90
## 107  47913.00  62237.30  28028.00  28053.10  28614.70
## 108  37346.20  36901.50  24029.60  24677.10  25195.80
## 109  47831.90  41292.50  24739.00  24680.80  23879.80
## 110  28144.90  28366.40  26321.90  27099.90  23105.10
## 111  52370.60  52892.70  52604.60  28917.50  23506.70
## 112  56818.20  55409.00  51108.00  37084.80  36251.20
## 113  20587.60  19530.00  12707.00  86300.00  81814.50
## 114  51009.60  46019.80  43031.20  50820.70  44322.00
## 115  44678.60  43028.50  41019.40  20480.70  20504.30
## 116
ddata3 <- decompose(tsdata3)
plot(ddata3)

plot(ddata3$seasonal)

plot(ddata3$trend)

plot(fsal$avg_wage_ft,type ="l",main = " Female Average salary",xlab= "Year",ylab="Average Salary")

#class

class(tsdata3)
## [1] "ts"

arima model

mymodel3 <- auto.arima(tsdata3)
mymodel3
## Series: tsdata3 
## ARIMA(1,0,3)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1      ma2      ma3     sar1       mean
##       0.9026  -0.0696  -0.1209  -0.4726  -0.0365  43185.427
## s.e.  0.0397   0.0482   0.0423   0.0356   0.0276   1406.228
## 
## sigma^2 estimated as 248663125:  log likelihood=-15372.11
## AIC=30758.22   AICc=30758.3   BIC=30794.86

fitting model

auto.arima(tsdata3,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 30742.64
##  ARIMA(0,0,0)            with non-zero mean : 31782.99
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 30909.8
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 31195
##  ARIMA(0,0,0)            with zero mean     : 33887.97
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 30769.33
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 30741.01
##  ARIMA(2,0,2)            with non-zero mean : 30768.15
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 30744.25
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 30742.41
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 30741
##  ARIMA(1,0,2)            with non-zero mean : 30767.52
##  ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 30744.23
##  ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 30742.65
##  ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 30768.92
##  ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 30742.88
##  ARIMA(0,0,2)(1,0,0)[12] with non-zero mean : 30741.56
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 30886.4
##  ARIMA(1,0,3)(1,0,0)[12] with non-zero mean : 30728.68
##  ARIMA(1,0,3)            with non-zero mean : 30757.41
##  ARIMA(1,0,3)(2,0,0)[12] with non-zero mean : 30733.8
##  ARIMA(1,0,3)(1,0,1)[12] with non-zero mean : 30730.29
##  ARIMA(1,0,3)(0,0,1)[12] with non-zero mean : 30757.52
##  ARIMA(1,0,3)(2,0,1)[12] with non-zero mean : 30731.33
##  ARIMA(0,0,3)(1,0,0)[12] with non-zero mean : 30741.16
##  ARIMA(2,0,3)(1,0,0)[12] with non-zero mean : 30731.32
##  ARIMA(1,0,4)(1,0,0)[12] with non-zero mean : 30730.3
##  ARIMA(0,0,4)(1,0,0)[12] with non-zero mean : 30740.4
##  ARIMA(2,0,4)(1,0,0)[12] with non-zero mean : 30733.43
##  ARIMA(1,0,3)(1,0,0)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,3)(1,0,0)[12] with non-zero mean : 30758.22
## 
##  Best model: ARIMA(1,0,3)(1,0,0)[12] with non-zero mean
## Series: tsdata3 
## ARIMA(1,0,3)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1      ma2      ma3     sar1       mean
##       0.9026  -0.0696  -0.1209  -0.4726  -0.0365  43185.427
## s.e.  0.0397   0.0482   0.0423   0.0356   0.0276   1406.228
## 
## sigma^2 estimated as 248663125:  log likelihood=-15372.11
## AIC=30758.22   AICc=30758.3   BIC=30794.86

Residuals

library(tseries)
adf.test(mymodel3$residuals)
## Warning in adf.test(mymodel3$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel3$residuals
## Dickey-Fuller = -9.9963, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel3$residuals)

acf(ts(mymodel3$residuals),main = "ACF Residual")

pacf(ts(mymodel3$residuals),main = "PACF Residual")

#forecasting female salary and mean

myforecast3 <- forecast(mymodel3, level =c (95), h= 3*12)
plot(myforecast3,main = " Female Salary forecasting for the next 3years",xlab = "Time", ylab ="Average salary")

myforecast3[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 116                                                               
## 117 42675.15 42362.16 42468.03 42542.80 42186.18 42291.13 42372.07
## 118 42815.60 42864.88 42895.17 42923.27 42964.12 42985.41 43005.12
## 119 43085.40 43094.66 43103.54 43111.52 43118.16 43124.73 43130.63
##          Aug      Sep      Oct      Nov      Dec
## 116 39983.05 41174.03 41456.86 42383.04 42541.14
## 117 42653.84 42673.53 42720.23 42737.88 42778.56
## 118 43015.29 43033.04 43048.00 43062.40 43074.50
## 119

calculating average women salary

avgsal<- 53392.66/43074.50
avgsal
## [1] 1.239542
cat("The average mens salary is ",avgsal,"times of womens salary")
## The average mens salary is  1.239542 times of womens salary

calculating how much women earns for every men who earns a dollar

cents <- 1/avgsal
cents
## [1] 0.8067495
cat("The women earns",cents*100, "cents for every men who earns a dollar")
## The women earns 80.67495 cents for every men who earns a dollar

forecasting for all races: dataset 2

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
allrace <- subset(newrace, select = c(avg_wage, year))
head(allrace)
allrace$avg_wage <- as.numeric(allrace$avg_wage)
allrace$Date <- paste (allrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata4 <- ts(allrace$avg_wage,frequency = 12)
tsdata4
##             Jan         Feb         Mar         Apr         May
## 1    66809.0000  48178.6000  44442.7000  57379.2000  52914.1000
## 2   160712.0000  99637.8000  41850.1000  50330.3000  19293.5000
## 3    51690.6000  80406.1000  47527.0000 161415.0000  37612.2000
## 4    44766.0000  93734.2000  70695.0000  84248.5000  56351.9000
## 5    31928.0000  39364.5000  86738.5000   8654.8500  59888.0000
## 6   122884.0000  37274.9000  87813.2000 118473.0000  49086.0000
## 7    32824.1000  82837.4000 131296.0000  41933.2000  72926.1000
## 8    32023.5000  26625.0000  45953.7000  30002.7000   5689.5100
## 9    32155.8000  80820.6000  54561.0000  58975.5000  58794.7000
## 10   50983.0000 117975.0000  56568.7000 108226.0000  51201.9000
## 11    1466.7400  46748.3000  27099.0000  43154.8000  13504.2000
## 12   28264.3000  32600.9000  91407.8000  93783.5000  41939.4000
## 13   14201.3000   8529.9300  17485.3000  36797.4000  28856.0000
## 14   31060.4000  26057.3000  27017.0000  32155.8000  25774.3000
## 15   17673.2000  45018.2000  13070.1000  22101.2000  25114.5000
## 16   41063.1000  22047.0000  22133.8000  10289.9000  24490.3000
## 17    8665.7400  24499.9000  23667.2000  25724.7000  18151.7000
## 18   40337.0000  31450.0000  25819.2000  30693.7000  29707.0000
## 19   55755.3000  46985.7000  23193.8000  53355.0000  41666.2000
## 20  202415.0000  32432.7000  51243.7000  65648.2000  66562.4000
## 21   32997.5000  28201.2000  32892.9000  21882.7000  64311.7000
## 22   48242.5000  32813.8000  49236.1000  43009.6000  70699.5000
## 23   33660.9000  39696.7000  24633.2000  28572.5000  12684.3000
## 24   57543.2000  42729.0000  54186.6000  28061.4000  25926.2000
## 25   30049.6000  28036.2000  43135.9000  44282.0000  31726.6000
## 26   22439.5000  52518.5000  26042.5000  21799.3000  27202.7000
## 27   12393.9000  10302.8000  17997.6000  18073.9000  14145.3000
## 28   21054.6000  40469.7000  28939.0000  34373.8000  26414.3000
## 29   17920.6000  44362.4000  46312.0000  67707.5000  50330.3000
## 30  126053.0000 125508.0000   7373.0700  74392.3000  96135.4000
## 31   83792.4000  33228.7000  63540.9000  61436.8000  20168.5000
## 32   82816.1000  54893.7000  43300.7000  37499.5000  53821.1000
## 33   24289.2000  10240.4000  20085.2000  19307.4000  43785.6000
## 34   39021.7000  47757.3000  49911.0000  99108.5000  41217.6000
## 35   10433.5000   8798.6400  22490.6000   8682.0700  12478.5000
## 36   97593.5000  53593.1000  51115.8000  42219.4000  95832.2000
## 37   92056.7000  44054.5000  79293.7000 180532.0000  72886.5000
## 38   77374.7000  40411.7000  37210.9000  47276.8000  44185.0000
## 39  132140.0000  82468.5000  88718.0000  64582.5000  89303.1000
## 40   75928.9000  67288.0000  98891.7000  70750.4000  72261.7000
## 41   42826.0000  76178.3000  59585.7000  61442.2000  75588.8000
## 42   50600.6000  43333.3000  28130.1000  64392.6000  49667.7000
## 43   22326.0000  30581.5000  35311.4000  27489.2000  23558.9000
## 44   55211.3000  35012.4000  68379.9000  88540.6000  65810.2000
## 45   26796.5000  17572.5000  17727.4000  14091.0000  11187.7000
## 46   19539.2000  43753.1000  62135.0000  41609.2000  75902.7000
## 47    7291.5800  35629.6000  36389.5000  27029.6000  22088.1000
## 48    8710.2200  35825.9000  34381.4000  10634.2000  33008.7000
## 49   49701.9000  43100.8000  36457.9000  30252.8000  69671.0000
## 50   65387.8000  51838.6000  51648.9000  72486.5000  57271.7000
## 51   26071.1000  26219.1000  30012.1000  26461.4000  36987.8000
## 52   21259.7000  22423.4000  21704.3000  22873.6000  16412.0000
## 53   27451.6000  22981.2000  18252.9000  22985.1000  77953.6000
## 54   37515.1000  27079.0000  24522.8000  21959.9000  42574.4000
## 55  137452.0000 136167.0000 121638.0000  50598.4000  71564.1000
## 56   70032.4000  39388.9000  82975.2000  77868.1000  76376.7000
## 57   29488.4000  13129.6000  38228.5000  34660.8000  29613.9000
## 58    1310.9500  10249.7000  13231.4000  16821.7000  12127.1000
## 59   21819.0000  19338.2000  25141.3000  13129.6000   4006.7500
## 60   39549.8000  30493.8000   5104.1000  34424.1000  34236.1000
## 61   23808.9000  22675.7000  14967.0000  14971.5000  37676.5000
## 62   49153.8000  49350.8000  33419.4000  40961.5000  35520.8000
## 63   58261.5000  35975.4000  36657.5000  26684.1000  26689.3000
## 64   30413.9000  37515.1000  60868.5000  39143.3000  74366.9000
## 65   15683.3000  39348.1000  36712.9000  58089.0000  37305.5000
## 66   28437.2000  36073.8000  22692.5000  42254.6000  54425.2000
## 67   37750.5000  37646.6000  72093.7000  56603.6000  85748.9000
## 68   12101.1000  60550.0000  43765.4000  30721.1000  33853.8000
## 69   39981.0000  14661.4000  53007.4000  50559.8000  42353.9000
## 70   17248.2000  71670.9000  56102.0000  89440.6000  27353.4000
## 71   32167.6000  43032.9000  42030.5000  20975.2000  12895.7000
## 72   22831.4000  17143.2000  56124.5000  42865.3000  28986.0000
## 73   64179.3000   5173.6000  24125.0000  20243.6000  26055.6000
## 74   46365.6000 124233.0000  83879.9000  55608.1000  67295.9000
## 75   59113.1000  10941.4000  94479.7000 115978.0000  59388.2000
## 76   52192.2000  42511.8000  35355.3000  52082.7000  39304.4000
## 77   64227.8000  34759.4000  40995.3000  26109.8000   6144.2200
## 78   20480.7000  46352.5000  22788.0000  20951.1000  55818.5000
## 79   27099.3000  32291.3000  30906.3000  36307.3000  36941.6000
## 80   52888.2000  48248.7000  44471.5000  54725.2000  45403.4000
## 81   69744.9000  46606.4000  40054.6000  23713.9000  45025.2000
## 82   27364.0000  36486.2000  74828.5000  58405.1000  55463.4000
## 83   53593.1000  41930.5000  38029.4000  54452.7000  49156.2000
## 84   31323.2000  42736.0000  36063.4000  35806.5000  39716.1000
## 85   71814.7000  42638.4000  39105.4000  36554.2000  38062.3000
## 86   19584.8000  12143.2000  33332.9000  13076.2000  14265.4000
## 87   30757.0000  27078.0000  19650.7000  28528.8000  16775.7000
## 88   17178.7000  31817.8000  46193.3000  29170.3000  38266.0000
## 89   47665.1000  12288.4000  41947.4000  10507.0000  39492.0000
## 90   26219.1000  86680.2000  24148.2000  55736.8000  99331.6000
## 91   69852.5000  62286.2000  51201.9000  69763.1000  61442.2000
## 92   16525.2000  19896.3000  16412.0000  28143.0000  14964.5000
## 93   21221.2000  23362.5000  19683.4000  25799.2000  22101.0000
## 94   47018.2000   6249.9200  31536.7000  30035.1000  26466.5000
## 95   12035.5000  25712.2000  22276.3000  25884.0000  13943.9000
## 96   42185.0000  40284.5000  49211.1000  29938.4000  35956.5000
## 97    7717.4000  28217.2000  16318.5000  16651.8000  17050.1000
## 98   47555.9000  26939.8000  32326.8000  36261.4000  21437.2000
## 99   10145.7000  18601.5000  19983.5000   3829.4800  11189.1000
## 100  15865.0000  40476.8000  75589.9000  58806.6000  79424.1000
## 101  50648.5000  45981.2000  50048.5000  44203.3000  38047.1000
## 102  71540.1000  57202.2000  79917.0000  74405.1000  66713.0000
## 103  31553.9000  45350.2000  45018.2000  36304.8000  30246.3000
## 104  79270.1000  57878.4000 133983.0000  78052.4000  43946.3000
## 105  38315.4000  43319.4000  29859.7000  23247.1000  26259.3000
## 106  32641.9000  33091.0000  63982.9000  46366.5000  55923.1000
## 107  42510.2000  25018.2000  27525.3000  24576.9000  41159.1000
## 108  17173.3000  18345.5000  20476.3000  28954.6000  24147.2000
## 109  20565.1000  21327.6000  15638.7000  23202.4000  22796.7000
## 110   5647.1800  19774.1000  12961.8000 135886.0000  86012.1000
## 111  42671.3000  31438.8000  34689.9000  20113.5000  20299.3000
## 112   8360.5200   5405.7700  37489.2000  37774.7000  32603.8000
## 113  27227.5000   7398.7700   9075.8300  28132.9000  35094.9000
## 114  38421.0000  23962.5000  35416.2000  61987.9000  66225.0000
## 115  38184.0000  48129.7000  28742.1000  18958.1000  55895.3000
## 116  23289.4000  10035.6000  23580.9000  29595.8000  30558.7000
## 117  17353.5000  36561.5000  25600.9000  20812.3000  17706.1000
## 118  12115.2000  24600.7000  36729.3000  31053.6000  31113.1000
## 119  82116.0000  49846.1000 157564.0000  35992.4000  75341.8000
## 120  61853.5000  37652.4000  41666.2000  57994.8000  53241.9000
## 121  25030.4000  27449.4000  14189.1000  30252.8000  22052.2000
## 122  42707.8000  59250.7000  76597.6000  62656.8000  81844.1000
## 123  27017.8000  20030.4000  29803.8000  25060.5000  30459.3000
## 124  47865.6000  73688.0000  62835.6000  72118.7000  28389.6000
## 125  32863.6000  31257.8000  29559.6000  41366.7000  28198.9000
## 126  37322.5000  26645.8000  32824.1000  40763.9000  27974.5000
## 127  82654.7000  73144.6000  76589.5000  48957.7000 109353.0000
## 128  46188.0000  41963.8000  40859.5000  48614.0000  14239.5000
## 129  22647.9000  15120.3000  22170.1000  27038.0000  24899.1000
## 130  26309.9000  41101.0000  14643.7000  17683.7000  29327.9000
## 131  71179.0000  92875.6000  73623.1000  16129.3000  17984.9000
## 132  63709.8000 161348.0000  12004.3000  18033.0000  11017.8000
## 133  50965.3000  33379.8000  24443.5000  32753.9000  32093.2000
## 134   5949.7100  48236.4000  40547.9000  26420.2000  36212.1000
## 135  21261.4000  28525.8000  23962.1000  81584.9000  60614.2000
## 136  39343.2000  36874.6000  20474.2000  24478.9000  70491.0000
## 137  30148.0000  34183.5000  31797.4000  27698.5000  17783.1000
## 138  30852.6000  19747.4000  20305.0000  38587.0000  18711.6000
## 139  13714.6000   9374.8900  28429.0000  32400.5000  30416.4000
## 140  48940.8000 252377.0000  21566.9000  20228.5000   1354.1500
## 141    416.6620  20275.9000  22024.9000  35208.3000  25737.9000
## 142  41337.8000  18993.5000   9371.3900   2048.0700   5042.1200
## 143  24523.0000  24051.1000  12158.9000  16697.8000  14802.0000
## 144  13315.4000  99752.8000  56419.5000 184327.0000  95321.4000
## 145  39557.8000  11790.5000  36303.3000  60989.8000  30613.1000
## 146   4299.2300  12869.6000  14438.9000 143760.0000 148458.0000
## 147 158973.0000  26439.1000  23749.8000  23022.5000  24713.8000
## 148  85748.9000  45283.0000  65547.6000  27269.6000  27321.5000
## 149  22492.5000  16077.9000  42748.1000  10979.1000  20904.7000
## 150  35313.7000  25724.7000   6041.5900  44157.9000  30635.8000
## 151  43009.6000  71170.3000  67355.8000 109594.0000  57047.0000
## 152  54065.1000  34856.8000  47690.6000  35342.1000  66885.2000
## 153  45239.3000  31073.5000  21378.0000  36613.4000  54856.7000
## 154   9374.8900  40097.0000  35948.0000  32039.2000  32109.0000
## 155  44306.5000  73997.6000  36505.4000  36342.5000  22429.3000
## 156   2381.4100  20833.1000  16081.3000  16049.6000  19385.0000
## 157  24730.9000  35203.8000  25888.3000  42124.1000  25535.9000
## 158  19293.5000  48355.4000  40803.8000  18619.6000  20688.0000
## 159  16197.6000  15968.8000  35316.6000  20759.3000  32269.6000
## 160  36150.8000  33042.9000  45609.3000  35371.4000  25662.8000
## 161  18990.2000   6855.4100  20276.8000  19944.0000  14889.1000
## 162  34714.9000  20833.1000  83531.3000  45287.6000  44552.5000
## 163 145832.0000 107186.0000  98904.9000  99467.3000  88119.0000
## 164  17070.9000  12862.3000  27557.3000  29018.6000  13480.5000
## 165  19688.1000  21950.6000  19433.7000  21874.7000  51103.7000
## 166  66559.7000  55792.6000  15317.9000  70598.7000  51782.5000
## 167 201876.0000 181223.0000  52922.0000 214449.0000 129635.0000
## 168  57346.1000  30656.6000  26082.6000  25985.2000  24603.5000
## 169  36203.8000  23193.8000  24055.9000  57251.3000  61177.8000
## 170  43198.4000  52289.4000  49373.0000  44399.4000  55093.2000
## 171  54906.2000  45792.7000  36558.0000  51563.6000  84842.7000
## 172  33705.6000  39629.0000  55005.8000  38541.2000  25308.4000
## 173  18674.3000  21806.7000  20521.5000  16525.3000  25600.9000
## 174  12605.3000  28643.9000  32072.0000  29406.5000  25962.1000
## 175  44212.7000  38224.4000  25210.6000  30495.6000  62278.8000
## 176  39898.2000  51331.5000  42893.4000  38927.8000  33662.7000
## 177  35520.4000  59456.6000  35221.9000  51930.3000  62577.0000
## 178  53266.1000  58013.7000  47134.7000  96467.5000  75892.1000
## 179  17781.7000  70658.6000  43010.4000  48533.9000  56536.7000
## 180  56130.9000  56162.0000  50281.7000  40463.7000  63951.0000
## 181  71682.6000  33889.3000  55148.9000  27305.7000  15626.8000
## 182  25983.2000  20905.9000  20898.3000  24017.6000  24957.0000
## 183  20398.7000  43698.8000  22939.1000  14223.8000  22976.9000
## 184  69423.9000  52516.2000  67603.0000  56765.7000  55511.7000
## 185  27868.4000  37719.6000  41542.5000  44834.3000  11148.5000
## 186  51429.7000  56363.3000  60505.5000  53802.5000  28964.2000
## 187  19407.7000  42704.4000  41546.3000  29812.1000 396311.0000
## 188  36335.9000  58148.6000  30789.1000  39395.5000   2457.6900
## 189  37789.2000  48096.4000 156039.0000  34266.7000  71767.8000
## 190  50881.3000  43383.9000  23091.7000  51553.7000  25600.9000
## 191  39821.0000  31718.6000  30673.0000  28579.3000  28228.7000
## 192  47791.5000  56657.3000  55950.6000  89470.3000  39987.3000
## 193  34472.8000  37830.6000  29747.2000  27093.2000  23782.7000
## 194  32093.9000  35541.8000  16412.0000  33186.5000  39683.9000
## 195  26807.9000  36205.9000  15541.8000  27857.7000  18432.7000
## 196  37499.5000  29345.2000  15591.1000  48194.3000  66003.8000
## 197 196615.0000  50494.2000   8089.8900  34453.4000  42171.1000
## 198  84351.8000  73941.9000 110785.0000  77822.8000  60418.2000
## 199  44077.0000  35359.8000  57276.7000  50042.5000  57024.3000
## 200  38615.8000  37431.8000  17223.8000  82060.2000  72541.7000
## 201  21015.0000  18216.4000  33890.3000  29797.3000  30955.4000
## 202  20611.6000   3455.3400  49165.0000  44038.0000  36012.4000
## 203  21580.3000  46833.4000  29301.9000  93001.6000  25654.9000
## 204  25165.1000  54200.7000  53187.1000  48233.7000  31660.0000
## 205  27393.4000  22854.5000  22314.1000  26435.2000  36208.6000
## 206  18045.4000  16841.9000  57922.2000  32739.9000  26802.8000
## 207  16474.5000  23817.0000  12971.2000  17392.0000  24749.4000
## 208  41483.5000  17042.6000  45299.8000  23464.0000  26325.2000
## 209  35757.1000  22291.4000  42783.2000  57049.6000  26796.5000
## 210  17668.9000  78528.7000  67347.8000  70832.5000  75417.4000
## 211  56534.1000  33108.9000  25202.9000  24783.9000  12862.3000
## 212  35408.1000  44394.6000  36493.0000  29186.9000  49903.2000
## 213  36608.9000  34379.0000  33060.9000  38841.8000  41879.3000
## 214  72433.3000  68374.8000  23228.9000  53937.0000  80966.1000
## 215  58626.1000  35534.9000  22352.5000  30399.1000  32155.8000
## 216  16946.3000  47928.3000  42124.2000  53057.1000  57195.8000
## 217  28607.9000  30261.4000  22185.3000  30012.1000  31976.7000
## 218  29285.7000  31133.6000  31119.6000  19059.3000  40731.3000
## 219  31008.7000  25600.9000  28050.2000  25673.9000  10072.3000
## 220  51799.4000  33270.2000  88959.2000  64453.7000  61796.3000
## 221  82941.0000 126198.0000 124900.0000   7381.5500  82588.4000
## 222  58742.0000  57606.0000  84397.4000  75481.7000  66044.6000
## 223  37015.9000  47730.1000  97743.1000  76071.6000 121260.0000
## 224  76613.8000  57061.2000  58210.3000  71645.4000  55594.9000
## 225  75390.7000 177061.0000 191568.0000  55765.8000  80558.9000
## 226  40089.1000  83197.7000  42591.3000 135571.0000  53654.7000
## 227  71461.1000  66839.2000  70913.9000  72242.6000  59565.5000
## 228  70991.5000  53058.7000  80493.2000  88642.4000  79327.9000
## 229 116643.0000  58157.1000 108377.0000  51260.8000 247168.0000
## 230  29427.8000  50682.7000  41924.4000  77578.2000  27087.1000
## 231  79202.7000  74811.0000  87010.5000  91476.0000  57174.1000
## 232  35699.4000  52624.9000  34175.3000  50085.7000  63468.5000
## 233  26190.3000  33892.7000  81803.2000  62371.9000  56568.1000
## 234  63614.0000  70088.5000  16750.6000  53305.3000  32306.7000
## 235  49091.9000  45245.3000  14763.1000  70739.8000  42832.0000
## 236  89853.1000  44836.7000  45273.8000  63263.3000  51835.5000
## 237  48747.6000  49951.9000  56102.8000  46020.2000  41775.0000
## 238  28398.4000  47059.4000  31693.4000  40565.4000  83352.5000
## 239  85796.8000  38396.8000  22401.9000  20000.8000   8839.6000
## 240  53537.6000  40700.5000  95942.4000  41818.7000  69751.1000
## 241  62020.9000  38635.1000  41714.1000  56028.4000  54904.5000
## 242  65030.4000  47484.0000  48285.8000  88920.7000  51610.7000
## 243  21461.9000  20588.1000  82179.1000  60104.6000  45860.6000
## 244   5047.9200  55936.9000 119806.0000  56122.9000  34754.8000
## 245  46102.8000  35661.0000  91589.2000  44250.0000 121172.0000
## 246  55997.9000  48289.2000  33083.5000  52878.3000  30255.4000
## 247  39702.2000 101926.0000  15378.2000  79485.1000  72970.4000
## 248  72835.7000  92066.6000  59080.4000 134137.0000  50063.2000
## 249  78446.4000  67663.7000  49367.0000  39802.2000  39666.8000
## 250  75446.2000  60487.7000  73602.1000  87895.7000  78330.9000
## 251  66154.3000  66439.2000  76253.9000  83962.3000  85293.1000
## 252  36045.5000  53408.8000  25630.4000  34967.0000  49525.6000
## 253  55335.2000 167746.0000 100666.0000  53067.0000  69528.3000
## 254  74413.9000  73783.1000  87110.0000  95351.4000  69751.1000
## 255  82286.8000  94076.4000  95417.0000  78166.6000  80101.1000
## 256  78213.9000  82948.6000  83463.3000  72590.3000  82768.7000
## 257  66438.6000  72177.3000  70047.3000  61162.5000  90704.1000
## 258  70577.5000  62584.4000  73879.3000  83822.7000 133678.0000
## 259  42756.9000  57889.4000  85941.6000  41652.3000 104300.0000
## 260  79183.8000  42155.5000  31598.8000  48060.7000  49076.7000
## 261  40360.4000  38001.9000  26378.0000  22027.8000  43278.4000
## 262  49944.8000  23220.5000  36149.3000  41714.1000  30797.3000
## 263  85399.8000  80482.0000  75939.2000  93891.4000  48492.7000
## 264  57971.4000  31789.6000  41269.9000  64071.4000  52571.1000
## 265  62686.5000  59643.7000  44930.1000  26178.7000  57978.9000
## 266 258722.0000  23355.6000  36027.7000  39043.4000  88262.5000
## 267  28299.0000  54203.4000  28168.7000  42589.6000  37832.4000
## 268  34843.5000  56181.4000  10802.6000  29128.3000   7299.9600
## 269  38728.2000  42388.5000  37643.3000  25239.6000  33476.1000
## 270  26217.9000  40621.5000  30874.9000  16706.1000  42898.7000
## 271  31170.9000   3836.2800  12514.2000  33574.0000  28216.7000
## 272 136615.0000  81991.0000 111608.0000  83699.4000 119381.0000
## 273  32957.4000  39868.5000  48271.9000  38153.3000  26450.5000
## 274  55433.6000  77483.9000  36289.2000  48285.9000  21478.6000
## 275  40433.4000  29090.3000  41595.6000  35199.8000  40233.8000
## 276  37780.8000  39817.1000  40482.7000  40064.6000  48391.3000
## 277  13752.0000  27807.9000  23860.1000  40099.2000  32280.6000
## 278  16267.0000  20003.6000  20862.7000  20343.4000   8370.1300
## 279  18869.7000  17569.1000  45681.4000  32427.6000  35335.5000
## 280  34765.8000  46647.2000  32374.2000  54507.8000  51501.6000
## 281  36252.7000  78444.2000  64385.6000  44959.2000  38249.4000
## 282  50161.2000  19539.1000  16060.6000  29812.1000  40358.4000
## 283   6257.9000  19594.6000  14814.4000  48232.3000  24236.7000
## 284  95276.9000  57356.9000  57704.4000  57856.2000  96578.5000
## 285  31416.0000  32235.2000  53424.3000  38410.6000  24272.2000
## 286  21008.8000  30997.0000  24347.5000  24685.6000  28909.9000
## 287  17145.0000  33908.7000  44002.6000  35213.4000  45066.1000
## 288 169385.0000 117159.0000 142046.0000 162850.0000  92666.0000
## 289  60205.9000  71959.4000  48060.7000  97079.9000  96484.5000
## 290 217811.0000 130404.0000 158423.0000 261509.0000  73687.5000
## 291  59327.7000  48064.8000  62003.8000  78679.0000  64958.4000
## 292  55959.7000  31915.5000  17333.6000  31239.4000  56719.5000
## 293  50739.9000  55244.7000  29278.0000  48984.4000  47882.1000
## 294 124760.0000  34279.3000  57097.5000  56217.2000  65537.1000
## 295 104039.0000 137893.0000  67918.8000  59020.2000  44452.7000
## 296  44545.2000 153588.0000  45668.5000  38471.2000  48331.4000
## 297  44995.2000  44807.1000  49239.2000  47494.3000  47882.1000
## 298  25239.6000  36447.6000  43887.5000  31931.8000  30718.3000
## 299  36106.9000  32019.4000  34866.0000  33978.6000  41008.6000
## 300  56112.7000  41999.7000  34928.0000  27699.4000   6048.5400
## 301  40545.0000  12302.6000  36106.4000  48676.5000  25559.0000
## 302  41504.5000  39204.7000  54068.3000  40184.8000  39716.1000
## 303  34875.0000  32040.4000  26349.3000  19506.1000  29532.4000
## 304  27051.3000  26965.8000  18980.2000  30044.0000  34343.4000
## 305  22005.3000  35422.8000  25138.7000  41714.1000  21060.0000
## 306  13439.9000  13841.5000  23187.3000  29418.4000  26624.2000
## 307  58399.7000  57511.4000  62493.7000  73492.3000  60633.7000
## 308  85708.7000  49834.8000  40061.3000  21250.1000  34504.0000
## 309  58848.1000  41711.3000  41369.1000  32039.4000  31656.3000
## 310  79260.1000  57072.6000  57985.0000  59507.0000  36793.5000
## 311  27561.7000   8010.1100   6509.4300  33196.3000  26655.6000
## 312  34079.3000  36088.7000  29292.5000  26839.8000  20694.7000
## 313  39811.6000  31689.9000  34327.6000  37248.7000  41051.8000
## 314  34195.4000  24973.8000  25099.7000  30831.7000  28504.9000
## 315  55522.7000  22949.4000  21908.8000  17406.8000  17276.2000
## 316  14538.4000  16352.0000  13521.9000  15436.1000  22320.4000
## 317  26586.3000  23985.8000  14849.0000  12615.9000  10744.8000
## 318  15019.0000   7989.5100   9132.7600   7753.4000  17577.7000
## 319  17985.7000  15912.6000  10194.4000  14546.1000  17961.3000
## 320  10880.4000   9200.3800  13034.6000  14110.5000   9962.7400
## 321    820.1720  17334.0000   8282.8500  36852.8000  33170.2000
## 322  17736.2000  31380.1000  29142.6000  54381.6000  15915.4000
## 323  13801.5000  20988.1000  19656.5000  17689.0000  18631.2000
## 324  28157.0000  23032.5000  22629.9000  21815.5000  19360.0000
## 325  16685.6000  31346.4000  47392.8000  20025.3000  40820.1000
## 326  35044.2000   6561.3800  19807.5000  24812.8000  12115.0000
## 327  32097.9000   8978.3100  25386.4000  12167.7000  10924.8000
## 328  10600.6000  16461.3000  13557.1000  24351.0000  11786.6000
## 329   9385.6700   7790.1400  11632.4000  23062.5000  19134.5000
## 330  38631.4000  19116.2000  23579.9000  17665.6000  18570.6000
## 331  13931.7000   5047.9200  31761.2000  32763.2000  13415.8000
## 332  20035.3000  15223.9000  37542.7000  27411.1000   1009.5900
## 333  27058.4000  23044.0000  11043.1000  15957.7000   8285.1800
## 334  15790.8000   9543.4400  47869.9000  37168.5000  32374.8000
## 335  29132.8000  24725.9000  67375.2000  19897.3000  51425.5000
## 336  16155.1000  10254.7000  27729.2000  17861.4000  20054.4000
## 337  35171.9000  28664.8000  18824.6000  28694.8000  21461.9000
## 338  26827.3000  29058.3000 100958.0000  19779.9000  46819.3000
## 339  41839.8000 103868.0000  47846.5000  69966.9000  66515.3000
## 340  62421.9000  37035.7000  43429.1000 141181.0000  39906.2000
## 341  62183.2000  45689.4000  18917.9000  10447.8000   2050.4300
## 342  44017.9000  41652.3000  48812.3000  91032.7000  77213.5000
## 343  22524.1000  24193.7000  42648.9000  10012.6000    363.4510
## 344  28895.7000  26031.2000  47287.7000  39930.6000  30522.2000
## 345   9756.7600  23830.0000  26585.2000   9067.6400  48778.9000
## 346  33076.2000   3204.0400  34307.5000  40383.4000  28886.6000
## 347  39628.4000  30539.9000  26919.9000  33075.2000  32156.9000
## 348  26249.2000  37245.7000  35371.6000  43994.6000  35412.1000
## 349  20025.3000  23317.5000  25624.7000   6174.1800  16096.4000
## 350  34795.8000  63751.3000  46552.2000  32808.7000  24030.3000
## 351  35440.1000  25800.0000  17690.6000  30037.9000  28421.1000
## 352  43817.0000  32025.6000  39736.1000  53032.3000  41714.1000
## 353  24047.1000  19188.2000  21689.4000  11719.5000  19024.0000
## 354  22787.0000  19267.3000  14495.3000  25031.6000  17144.9000
## 355  39994.1000  41967.7000  11804.0000  51310.5000  39024.3000
## 356  27559.3000  19507.8000  22098.7000  31414.0000  25085.0000
## 357  35628.4000  30501.3000  39784.3000  33991.6000  23709.8000
## 358  21324.5000  34233.0000  36983.4000  10764.8000  27546.2000
## 359  34448.6000  27785.7000  37577.2000  28043.2000  51251.3000
## 360  45698.9000  48060.7000  42003.4000  49868.6000  50059.4000
## 361  35777.0000  42927.1000  45453.7000  26352.1000  41228.7000
## 362  21781.8000  19826.4000  20575.1000  13636.1000  27342.3000
## 363  30046.6000  31774.4000  30287.6000  13424.8000  11505.3000
## 364  32840.9000  36019.9000  35012.0000  31813.0000  38281.8000
## 365  24813.8000  49061.9000  20504.3000  19600.7000  30709.8000
## 366  33617.0000  26460.0000  20653.3000  44675.7000  17070.3000
## 367  25735.8000   8510.7400  25728.4000  19640.1000  25893.4000
## 368  40020.9000  23039.8000  78530.1000  31434.1000  35205.3000
## 369  35504.5000  35329.9000  30037.9000  29420.4000   9619.7900
## 370  13123.1000   7762.0400  18081.1000  22462.7000  12506.2000
## 371  29378.9000  12302.6000  21625.6000   7631.2700  13755.0000
## 372  39077.2000  32432.2000  52142.6000  30394.7000  37141.5000
## 373  28658.9000  20306.7000  65082.2000  26688.3000  21505.1000
## 374  24494.4000  42402.6000  33222.5000  14199.7000  28149.4000
## 375  26910.4000  25054.5000   5150.8500  35947.7000  36868.2000
## 376  15193.3000  30742.7000  38402.2000  36542.2000  17117.3000
## 377  53757.8000  32612.9000  29716.3000  21783.5000  26827.3000
## 378  18801.9000  35776.1000  34332.8000  38782.4000  35720.9000
## 379  22130.9000  27013.7000  25302.9000  23220.5000  25754.3000
## 380  34098.5000  33999.1000  21962.5000  16560.7000  18010.7000
## 381  33402.5000  19865.1000  13664.9000  25202.6000  47937.2000
## 382  20025.3000  82052.7000  29704.3000  23161.8000  11160.2000
## 383  26827.3000  48337.9000  27427.7000  38185.3000  40237.8000
## 384  43658.5000  31288.2000  22027.8000  26827.3000  43574.1000
## 385  18771.3000  14896.0000  24183.1000  56457.4000  50068.9000
## 386  35674.6000  37933.0000  30517.4000  50725.7000  44608.0000
## 387  25970.0000  33824.6000  40525.9000  16690.2000  28258.5000
## 388  32954.1000   5214.2600  28469.9000  34650.9000  24422.2000
## 389  32357.8000  61360.5000  44034.2000  73263.7000  30448.9000
## 390  45295.3000  37374.3000  31566.9000  11482.4000  36023.8000
## 391  30777.3000  32192.8000  25972.9000  30565.1000  41394.3000
## 392  42656.7000  32040.4000  49267.7000  26698.2000  38751.1000
## 393  29008.8000  19341.9000    417.1410    100.1260  20299.3000
## 394  42467.7000  15017.1000  34005.9000  34240.3000  33457.5000
## 395  13306.0000  70088.5000  43445.0000  41386.7000  42581.3000
## 396  41107.1000  28706.0000  42202.2000  34968.7000  37542.7000
## 397  38585.5000  36631.4000  30275.8000  26357.9000  78213.9000
## 398  79966.8000  37168.5000  15262.8000  38465.2000  34529.8000
## 399  32617.0000  32577.7000  35595.2000  18994.2000  40121.2000
## 400  37453.8000  51550.8000  44456.7000  32277.9000  26924.5000
## 401  33445.8000  21645.4000  26381.3000  22399.3000  25630.4000
## 402  13859.9000  55262.6000   9902.5100  14724.0000  20813.6000
## 403  19531.1000  31168.0000  49210.3000  23451.0000  24703.9000
## 404  26850.5000  23103.4000  23016.9000  41272.9000  33055.7000
## 405 396767.0000  26072.0000  19530.3000  30143.0000  29844.7000
## 406  21354.6000  18779.1000  41330.5000  23111.3000  50063.2000
## 407  33229.1000  42402.6000  30405.3000  29278.0000  31828.4000
## 408  24447.4000  28460.5000  29439.6000  28369.3000  28734.5000
## 409  18284.9000  22511.7000  18193.4000   5653.6800  19308.4000
## 410  15749.5000  18729.2000  29482.6000  15608.0000  21753.0000
## 411  20349.9000   7008.8500  48507.3000   2002.5300  35501.8000
## 412  22694.2000  10299.5000  14599.9000  28882.9000  26551.7000
## 413  25358.3000  41171.2000  25630.4000  25804.8000  23481.9000
## 414  71765.0000  56140.7000  46208.9000  34380.5000  99125.1000
## 415  33717.3000  46633.4000  33574.4000  44699.4000  31877.3000
## 416  26249.2000  30046.6000  27449.6000  31468.0000  22995.8000
## 417  17162.9000  35636.0000  24255.5000  42136.1000  27237.0000
## 418  19647.3000  22027.8000  27059.5000  32433.1000  72999.6000
## 419  27930.4000  25463.9000  98939.3000  21124.3000  17121.6000
## 420  23896.3000  21899.9000  39898.6000  16902.0000  34244.8000
## 421  26827.3000  33028.3000  37709.2000  31262.9000  24886.4000
## 422  24004.0000  30651.9000  19408.1000  28800.8000  30721.1000
## 423  38886.6000  38258.4000  37046.8000 115885.0000  98093.9000
## 424 123026.0000  37673.1000  84796.3000 112642.0000  44567.4000
## 425  27604.3000   8509.2800  20710.7000  23346.4000  27774.4000
## 426  37674.5000  33369.7000  27013.7000  24998.2000  39197.5000
## 427  16209.4000  14849.1000  21351.6000   8201.7200  59373.6000
## 428   3630.2000  67635.9000  30643.8000   6653.1800  78213.9000
## 429  17940.5000  21067.3000  11927.9000  14801.5000  16273.1000
## 430  44259.4000  26837.4000  39628.4000  37483.9000  74299.8000
## 431  42108.8000  21978.6000  48168.4000  43399.2000  36499.8000
## 432  31612.8000  46134.7000  45069.9000  36348.1000  30723.0000
## 433  21570.2000  18642.1000  21248.8000  21762.0000  16277.0000
## 434  26322.8000   7503.4500  23608.1000  18107.9000  16832.8000
## 435  39628.4000  26108.6000  26247.3000   6323.0000  20248.7000
## 436  24407.5000  24507.0000  82479.9000  74548.8000  49014.0000
## 437 117795.0000  66246.2000  64545.1000  29872.6000  29525.0000
## 438  40027.5000   2294.2700  10428.5000  62552.0000  72999.6000
## 439  99868.5000  69477.0000  79006.2000 145273.0000 100552.0000
## 440  46153.1000 349546.0000  45447.9000  48615.5000  88034.7000
## 441  45233.7000  62989.7000  67720.3000  49696.9000  56691.8000
## 442  60455.3000 102722.0000  75073.3000  90936.8000  57140.9000
## 443  58081.6000  69542.2000  42241.2000  45939.1000  56794.6000
## 444  27147.0000  54751.5000  20064.6000  37429.2000  49344.6000
## 445 125447.0000 112806.0000  76650.1000  51883.8000  41356.9000
## 446  47595.7000  40491.8000   9393.5500  33571.7000  89926.3000
## 447  53795.6000  64669.5000  61300.0000  44520.3000  55095.8000
## 448  48276.1000  34476.4000  21529.2000  24570.1000  45727.5000
## 449  35841.7000  47252.7000  36529.7000  22294.4000  79405.8000
## 450  42938.4000 186871.0000  67663.3000  62810.0000  55623.9000
## 451 105603.0000  38660.4000  38539.7000  57481.0000  54679.8000
## 452  84341.8000  48276.4000  54475.3000  63259.0000  52754.8000
## 453  38855.9000  22341.3000   7392.2100  37450.6000  18014.0000
## 454  52201.8000  55330.1000  42855.3000 101392.0000  38017.1000
## 455  48710.4000  61388.8000  76084.1000  58148.4000  61684.8000
## 456  49682.8000  47728.8000  44875.0000  55386.4000  43429.3000
## 457 100759.0000  13180.9000  17426.6000  65921.0000 313544.0000
## 458  29537.1000  55241.2000  48698.3000  14187.1000  41612.9000
## 459  43952.7000  17292.5000  86239.7000  68452.4000  42712.9000
## 460  44388.8000  44612.3000 101510.0000  57740.7000  48517.4000
## 461  44047.3000  58129.9000  62758.7000  29403.6000  55930.9000
## 462  52879.7000  83999.6000  52476.2000  74796.6000  15255.0000
## 463  85369.9000  60200.2000  43700.8000  55417.3000  84410.4000
## 464  30227.6000  78418.0000  30417.5000 115186.0000  57068.6000
## 465  22842.8000  28386.9000  27313.8000   2919.1300  78270.7000
## 466  74317.7000  59726.9000  62290.2000  76768.7000  86899.1000
## 467  76591.4000  69914.7000 100476.0000  75496.5000 113386.0000
## 468  64385.0000  82429.0000  73146.0000 124088.0000  81210.9000
## 469  65264.3000  66090.7000  60104.3000  68695.8000  90682.9000
## 470  90835.7000  51851.7000  43921.5000  31739.5000  36501.0000
## 471  43603.1000  79139.7000  64759.0000  94738.2000  60954.4000
## 472  44607.9000  81124.8000  56195.2000  37080.1000  73963.9000
## 473  28098.1000  20151.8000  92637.1000  72085.7000  67882.2000
## 474  90102.6000  73380.8000  74852.5000  79202.2000  92004.8000
## 475  90141.4000  94880.6000  86810.3000  66799.3000  73018.9000
## 476  72797.9000  60594.5000  81113.3000  87416.2000  71734.8000
## 477  56048.1000  83817.1000  75548.0000  74669.6000  43297.2000
## 478  71792.3000  88667.7000  90781.8000  93204.8000  77209.2000
## 479  53713.9000  42938.4000  43926.4000  40522.7000  34315.8000
## 480  65290.6000  19499.7000  47962.6000  50779.5000  85866.8000
## 481  90137.9000  64760.2000  29220.1000  43493.4000  73113.9000
## 482  69880.7000  59040.0000  57923.9000  50985.8000  56074.3000
## 483  33949.4000  85761.6000 100238.0000  51176.1000  28389.6000
## 484  66293.7000  54190.8000  69519.3000  47860.4000  55461.0000
## 485  11083.5000  50379.4000  39235.1000  39716.1000  50013.6000
## 486  44272.4000  37957.6000  45626.2000   1038.1700  39291.7000
## 487  11460.2000  32320.6000  31552.4000  41736.5000  40361.8000
## 488  25558.6000  29068.8000  35904.4000  28596.4000  71891.0000
## 489  34927.7000  33025.0000  19756.8000  43635.1000  40602.3000
## 490   2230.6200  35125.1000  45829.0000  27663.5000  48649.4000
## 491 117125.0000   6448.5600  86522.3000 111038.0000 129258.0000
## 492  40930.4000  47039.5000  38456.2000  26784.8000  41098.2000
## 493  63361.8000  37874.6000  47054.7000  21740.4000  21201.7000
## 494  25961.2000  34362.4000  33996.4000  40041.1000  36158.8000
## 495  40143.0000  40739.3000  50637.2000  45825.7000  35203.4000
## 496  21618.8000  41599.0000  32527.8000   1508.0300  41984.2000
## 497  24640.9000  29783.0000   2534.4700  18661.8000  23716.7000
## 498  35782.0000  14706.6000  42945.2000  72293.1000  41766.8000
## 499  46289.2000  32289.3000  44879.4000  38288.3000  41446.6000
## 500  38876.3000  83692.4000  52394.6000  56912.4000  37927.7000
## 501  50795.0000  19856.9000  16619.3000  32701.3000  36151.7000
## 502   6675.6200  16729.5000  18584.3000  56801.1000  28997.6000
## 503  65214.0000  47081.7000  84968.1000  46920.8000  35002.4000
## 504  40916.2000  29461.8000  48566.5000  51389.4000  36961.1000
## 505  19682.6000  31806.5000  24799.0000   8877.5400  28017.6000
## 506  39862.1000  45371.7000  48789.5000  47322.7000  78947.5000
## 507 429232.0000 116082.0000 126737.0000  47651.7000  31412.9000
## 508  48668.0000  95394.3000  94890.2000  89357.1000  64880.9000
## 509 160428.0000 226021.0000  82666.1000  52268.8000  90397.3000
## 510  62980.7000  64830.4000  52680.1000  78447.8000  15208.7000
## 511  31634.2000   8564.5000  56704.1000  66933.6000 109045.0000
## 512  63236.5000  70368.1000  43559.2000  38106.0000  47573.4000
## 513  57607.9000  57915.6000  54179.1000  67835.7000  63117.7000
## 514 139216.0000  67845.7000  71590.9000  63478.0000  43408.5000
## 515  70158.9000  50574.2000 157969.0000  45585.0000  39253.9000
## 516  96728.4000  47867.1000  43334.2000  51010.9000  49161.0000
## 517  26635.5000  36999.0000  23073.6000  31955.9000  33647.9000
## 518  35302.1000  32911.3000  37448.6000  35033.0000  41526.8000
## 519  55092.9000  42918.9000  34365.7000  29403.6000  10020.1000
## 520  41501.6000  42157.7000  33221.7000  50281.9000  25711.6000
## 521  44438.8000  39670.5000  54751.5000  49271.1000  41542.7000
## 522  30670.3000  35060.8000  32445.3000  25992.9000  25054.6000
## 523  27252.4000  24143.0000  19220.0000  29374.1000  31176.5000
## 524  22992.5000  35324.6000  16591.3000  42241.2000  45670.6000
## 525  20128.2000  23610.0000  28012.8000  27106.8000  23963.9000
## 526  39073.1000  65975.7000  76129.4000  68992.9000  56228.8000
## 527  49980.3000  43207.8000  11486.5000  25110.9000  36715.8000
## 528  66478.6000  58238.4000  70531.2000  42327.7000  41088.0000
## 529  80031.4000  57600.8000  59275.1000  58228.2000  35270.8000
## 530   8111.3300   6625.9600  34052.2000  30142.7000  30227.6000
## 531  31963.4000  24810.3000  29918.5000  20470.4000  22802.6000
## 532  39530.7000  39931.1000  32616.0000  28750.6000  26209.4000
## 533  31933.6000  33804.1000  26847.0000  18106.2000  35515.2000
## 534  18076.8000  49728.5000  37583.0000  23209.9000  25304.4000
## 535  15823.4000  13352.3000  15790.5000  17763.0000   7429.8300
## 536  24466.7000  25328.1000  24703.6000  15407.7000  12623.9000
## 537   5027.1900   8113.7500  10795.5000   7906.8900  18351.7000
## 538  18756.9000  16144.3000  14106.2000  15264.8000  18379.6000
## 539  10154.7000   9048.1900  12884.2000  14086.3000   8611.4200
## 540  17520.8000   9103.1500  38084.7000  29814.5000  37028.6000
## 541  13300.2000  31566.8000  37857.9000  51885.2000  16237.1000
## 542  14605.2000  21399.0000  20684.4000  15239.4000  20753.7000
## 543  28512.8000  21411.8000  25372.7000  23008.5000  21613.7000
## 544  16896.5000  31960.9000  47473.2000  20278.3000  41054.3000
## 545  20473.5000  23565.0000  12268.1000  21636.6000  25347.9000
## 546  22755.8000  12342.4000   6806.9300   7209.1400  16474.1000
## 547  14587.3000  13728.4000  22415.5000  13692.3000  52447.8000
## 548  10810.1000  12906.7000  23367.6000  20954.4000  39170.9000
## 549  20154.0000  35496.0000  17866.9000  26726.4000  23635.9000
## 550  27325.9000  27566.0000  13155.4000  13666.5000  18300.3000
## 551  16554.1000  38017.1000  18175.9000   1022.3400  29835.9000
## 552  24152.1000  10325.0000  15909.8000   5330.3300  22435.6000
## 553  23131.1000  48013.7000  37422.9000  29173.8000  42816.9000
## 554  68991.3000  19997.8000  52090.0000  71234.9000  14801.5000
## 555  29616.5000  22732.4000  24093.1000  20579.1000  30435.2000
## 556  18604.6000  30667.2000  10645.5000  19785.4000   6722.2800
## 557  43416.7000  61687.7000  43064.1000  64604.3000  38390.1000
## 558  65707.5000  99004.1000  34808.6000  33991.2000  44983.0000
## 559  35621.2000  57636.2000  60035.2000  73683.6000  39014.4000
## 560  50832.3000   6804.2800  54956.0000  59894.2000  31489.5000
## 561  96552.3000 147844.0000  20927.2000  17671.1000  21440.9000
## 562  29164.6000  20748.7000  50055.2000  30050.3000  28753.3000
## 563  28489.6000  41532.4000  53613.4000  28716.3000  36722.8000
## 564  29121.0000  14198.4000  43442.2000  42565.0000  35905.0000
## 565  30186.4000  19990.6000  31346.2000  38313.1000  23232.7000
## 566  33828.0000  28491.5000  30340.2000  24979.8000  11836.4000
## 567  46879.2000  45626.2000  38288.3000  66452.2000  20278.3000
## 568  35909.1000  35996.4000  44983.0000  66149.7000  55728.6000
## 569  33938.0000  50241.8000  29442.1000  22665.2000  24047.0000
## 570  23519.2000  19596.6000  45113.3000  31020.5000  35413.6000
## 571    817.8740  10727.3000  24671.2000  19011.1000  21877.0000
## 572  24916.1000  58030.6000  28212.5000  28510.4000  25149.3000
## 573  44978.8000  55341.4000  31999.2000  35451.1000  39668.3000
## 574  32059.3000  22211.2000  21625.7000  19844.0000  20000.7000
## 575  37699.0000  43113.2000  35488.7000  30309.2000  42938.6000
## 576  32324.5000  22691.7000  22166.9000  22166.9000  21593.9000
## 577  30367.4000  23058.8000  34632.6000  29892.3000  38711.8000
## 578  54196.5000  48474.0000  65904.5000  57724.1000  51817.5000
## 579  45403.8000  52469.7000  44137.8000  37457.1000  71002.2000
## 580  25002.8000  40351.3000  16168.7000  22236.4000  17340.6000
## 581  11027.6000  19661.0000  14311.2000  29426.8000  28885.5000
## 582  26511.1000  30843.6000  30628.4000  27780.9000  32661.5000
## 583  28676.5000  17118.2000  21508.2000  29688.0000  24514.4000
## 584  70974.1000  62290.2000  39642.9000  28161.2000  35011.9000
## 585  28578.9000  26061.5000  23287.6000  25907.1000   8618.2900
## 586  30085.3000  29293.5000  39372.6000  24162.1000  90460.8000
## 587  34041.8000  44866.2000  36616.0000  34862.2000  30417.5000
## 588  17722.3000  17850.2000  23513.9000  12964.3000  10189.9000
## 589  22688.6000  37053.6000  43311.4000  76655.6000  29750.2000
## 590  48794.0000  28479.7000  19219.7000  55053.8000  42650.7000
## 591  67481.0000  30818.3000  28678.6000  40201.0000  28324.6000
## 592  28705.6000  24521.1000  29090.7000  22858.8000  42938.4000
## 593  35265.6000  23791.7000  21470.2000  26673.9000  24496.3000
## 594  23001.1000  26229.4000  25189.7000  19453.2000   2146.9200
## 595  45626.2000  32976.0000  25305.5000  27867.8000  28936.8000
## 596  15572.5000  31145.1000  30844.9000  10048.9000  24883.1000
## 597  34174.6000  22668.2000  32607.9000  24738.9000  21240.8000
## 598  31204.2000  34635.4000  27326.6000  52831.4000  36246.0000
## 599  25618.9000  23399.0000  21986.2000  15567.3000  20244.5000
## 600  60905.1000  68392.1000  80665.4000  20278.3000  81113.3000
## 601  31855.7000  30453.3000  35668.6000  34210.5000  29729.2000
## 602  13701.8000  43058.3000  48119.8000  33150.3000  29297.6000
## 603  17309.4000  19008.5000  15161.0000  31265.9000  57466.3000
## 604  40355.8000  35575.5000  38412.3000  10075.9000  32854.4000
## 605  48279.5000  60311.9000  24513.0000  41890.8000  40360.4000
## 606  23236.7000     50.6958  32274.2000  39542.7000  33370.5000
## 607  40164.6000  39970.0000  21961.3000  14534.4000  41016.4000
## 608  38881.9000  46495.6000  48726.1000  33975.1000  39387.0000
## 609  34921.1000  28785.6000  26110.3000  30572.3000  40303.5000
## 610  39554.2000  33303.9000  44480.9000  39426.9000  32445.3000
## 611  29750.2000  48405.7000  28730.0000  20292.7000    422.4120
## 612  38338.5000  37003.8000  60455.3000 434970.0000  48694.7000
## 613  37706.2000  41170.9000  52588.4000  13296.5000  70974.1000
## 614  65206.2000  44728.9000  62290.2000  46395.0000  29068.8000
## 615  33370.9000  30567.5000  63543.7000  39073.1000  37423.5000
## 616  29995.0000  46539.6000  26400.8000  35265.6000  80977.3000
## 617  17806.3000  25557.7000  19154.3000  33210.1000  33020.3000
## 618  39530.7000  36717.5000  21801.6000  58354.1000  33078.5000
## 619  19090.6000  49261.9000  20432.2000  33340.2000  21576.0000
## 620  25338.5000  21569.5000  21491.4000  15309.7000  41395.0000
## 621  31024.4000  17422.2000  25580.2000  17581.1000  22577.9000
## 622  20859.0000  32475.2000  30799.5000  28351.2000  24775.2000
## 623  24209.0000  24182.1000  26400.8000  23393.6000 401780.0000
## 624  37939.6000  45412.4000  38050.7000  32607.0000  20151.8000
## 625  44226.0000  55765.4000  34914.3000  33655.1000  42938.4000
## 626  28477.2000  28108.5000  13570.1000  26517.3000  26366.9000
## 627  21323.3000  13101.2000  42135.3000  14222.4000  20399.0000
## 628  19981.8000  25954.2000  24955.4000  17749.0000  15948.5000
## 629  17519.9000  20487.6000  24745.3000  15852.1000  37374.1000
## 630  22599.0000  25937.0000  18136.6000   9684.3700  20796.1000
## 631  37514.9000  56228.8000  31718.6000  37514.9000  20503.5000
## 632  30346.7000  25954.2000  27173.8000  13298.5000  17221.2000
## 633  33587.1000  34464.6000  18535.4000  47106.2000  44210.3000
## 634  25889.8000  13903.9000  43003.7000  42623.7000  42957.3000
## 635  20617.9000  19396.8000  23657.6000   8305.3600  29839.9000
## 636  42189.5000  27799.7000  28899.6000  34947.3000  24536.2000
## 637  62303.3000  34123.5000  27177.7000  25954.2000  34166.6000
## 638  18989.4000  14836.3000  38154.2000  35214.7000  36183.8000
## 639  60455.3000  38907.1000  44510.9000  23388.1000  17917.1000
## 640  32759.6000  20140.5000 105603.0000  19966.3000  19365.9000
## 641  30081.6000  23334.0000  23922.3000  48582.7000  46372.2000
## 642  92000.7000  71438.3000  77259.2000  79194.3000  63516.2000
## 643  70464.9000  41928.9000  33571.8000  34482.6000  62709.3000
## 644  29560.8000  35257.5000  35538.6000  34131.1000  23015.9000
## 645  16610.7000  31196.2000  29121.0000  27961.4000  18258.4000
## 646  60690.1000  72671.9000  26288.2000  22902.5000   3873.1000
## 647  18973.6000  91252.4000  55969.6000  16442.5000  27647.8000
## 648  13649.2000  20508.0000  67934.9000  49617.9000  20648.5000
## 649  17968.9000  29904.9000  38793.1000  30227.6000  35265.6000
## 650   5174.5500  13789.3000  29269.9000  32712.4000  45852.5000
## 651  19874.6000  10381.7000  26621.6000  16746.1000  21882.4000
## 652  22885.6000   9525.8400  30078.3000   6101.0900  18136.6000
## 653  44343.6000  30916.9000  40129.1000  25443.8000  27563.4000
## 654  37383.5000  28529.5000  24816.7000  85214.0000  74940.6000
## 655 112964.0000  61131.8000  64258.2000  31282.4000  32174.1000
## 656  43269.8000   2323.2700  10560.3000  63744.0000  73922.1000
##             Jun         Jul         Aug         Sep         Oct
## 1    73957.4000  51895.3000  49198.9000  32865.6000  29250.2000
## 2     6050.5500  62279.3000  53659.0000 164120.0000  64820.6000
## 3    42973.7000  56667.7000  32677.9000  26796.5000  29192.1000
## 4    76589.5000  79728.7000  95088.9000  60046.8000  35619.8000
## 5    19365.3000  55879.6000  40904.3000  21903.9000  85415.6000
## 6    45883.9000  55566.8000  42411.2000  56512.3000  48361.1000
## 7    56078.7000  32346.4000  27054.9000  26407.6000  42188.7000
## 8    32155.8000  46521.0000   6553.8400  49787.4000  15140.3000
## 9    20014.2000  18464.3000  70270.6000  56137.9000  89133.7000
## 10  104414.0000  90910.8000  74983.6000  36106.0000  33743.1000
## 11   34849.6000 107549.0000  38531.9000  37840.8000  73606.3000
## 12   47786.6000  91260.0000  65354.1000  58952.4000  18360.8000
## 13   13230.0000  64195.5000  36498.3000  47286.9000  31941.0000
## 14   30673.4000  43688.1000  25406.3000  52518.5000  28673.4000
## 15   41869.6000  41439.9000  28561.5000  31619.9000  32678.4000
## 16   10798.1000  17726.2000  20205.5000  25169.6000  19753.0000
## 17   27618.8000  28660.9000  22885.3000  31737.8000  33334.3000
## 18   22916.4000  12862.3000  15006.1000  31970.7000  39582.9000
## 19   30564.2000  36748.8000  31204.6000  50471.4000   2048.0700
## 20   33352.9000  31534.9000  43580.2000  24727.5000  34605.0000
## 21   22346.4000  17761.7000  44039.6000  34497.9000  42435.0000
## 22   63754.9000  45147.4000  69790.8000  39622.5000  38756.2000
## 23   15208.5000  17779.4000  27353.4000  27450.4000  28449.1000
## 24   13535.9000  37889.4000  30063.6000  20933.3000  27340.8000
## 25   25401.5000  21300.0000   6127.1600  30964.0000  27817.2000
## 26   19199.2000  49198.4000  32538.2000  24110.6000  23751.7000
## 27    9499.5100  28131.5000  25315.3000  42353.9000  32336.8000
## 28   32436.9000  96495.2000  93748.9000 202415.0000  98307.6000
## 29   68532.3000  55165.2000  52598.9000  52021.1000 133997.0000
## 30   13353.5000  17694.0000   3875.5900  17506.2000   8950.3100
## 31   86123.8000  68800.6000  55736.8000  10718.6000  73078.4000
## 32   89749.8000  43934.6000  46895.7000  19110.1000  20375.1000
## 33   39038.7000  34117.8000  36737.0000  37111.1000  44772.6000
## 34   33548.9000  27067.7000  33799.6000  29046.1000  79874.9000
## 35   10858.7000  71908.6000  64758.2000  35416.2000  32824.1000
## 36   46839.5000  38294.8000  40974.0000  64047.6000  52504.2000
## 37   43765.6000  95449.8000  79147.7000 116129.0000  10084.2000
## 38   52225.9000  74609.4000  82190.3000  41577.2000  82060.2000
## 39   86263.5000  84240.5000  37260.7000  34209.1000  28059.3000
## 40   59964.9000  51015.2000  41730.3000  30326.7000  51951.7000
## 41   85716.1000  52792.9000  73929.0000  40049.3000  38313.7000
## 42   45320.9000  55255.4000  43880.2000  56501.5000  35606.4000
## 43   15402.1000  29817.6000  30691.5000  82533.3000  21689.1000
## 44   49903.7000  43264.1000  35220.4000  65648.2000  41577.2000
## 45   20845.2000  16068.3000  18211.3000  16859.2000  28573.9000
## 46   43565.2000  49215.4000  38133.7000  34606.5000  29649.7000
## 47   20718.1000  40760.1000  29151.0000   8531.6500   8736.6700
## 48   34002.8000  26558.1000  36989.1000  29952.3000  26968.9000
## 49   62771.5000  70533.2000  53148.2000  47626.7000  38084.6000
## 50   11067.7000   8590.1700  11092.7000   5999.3500  17042.4000
## 51   28867.2000  24314.0000  21868.1000  21036.8000  27099.0000
## 52    8192.3000  22786.7000  17518.3000  50103.6000  14804.0000
## 53   54174.6000  85617.6000  60902.5000  46688.6000  28512.3000
## 54   46391.7000  20833.1000  42694.8000  43280.1000  44114.6000
## 55   46485.3000  34108.3000  44626.8000  48982.4000  31743.9000
## 56   48036.9000  43378.1000  93001.6000  48676.0000  46038.7000
## 57   32898.6000  35350.1000  35300.1000  12499.8000   7503.0300
## 58   33480.0000  30252.5000  37274.9000  37320.9000   7291.5800
## 59   16473.4000  17543.6000  42512.9000  33032.2000  48668.0000
## 60   39083.3000  60024.2000  33463.4000  28275.3000  25152.1000
## 61   37909.4000  30635.8000  44572.9000  55736.8000  92336.1000
## 62   31715.3000  31530.4000  65838.0000  58053.3000  52013.6000
## 63   89997.2000  85162.7000  81502.2000  65119.2000  76889.1000
## 64   45025.3000  28673.0000  24002.4000  28782.7000   8753.0900
## 65   37269.8000  32764.8000  26483.5000  31071.1000  56895.1000
## 66   27633.5000  42004.5000  41209.0000  38587.5000  36598.8000
## 67   42509.4000  32595.0000  48484.6000  52929.5000  47661.7000
## 68   46409.2000  46238.0000  31672.9000  38893.0000  25210.6000
## 69   39697.0000  42202.3000  46120.9000  30507.0000  24895.9000
## 70   63829.2000  31973.4000  59479.7000  52710.7000  37671.1000
## 71    5208.2700  30932.6000  25816.6000  28447.5000  31903.5000
## 72   84445.1000  31890.7000  22817.1000  11147.4000  10718.6000
## 73   25183.5000  81099.5000  56939.4000  83492.5000  22025.3000
## 74  120355.0000  80635.6000  68223.6000  63680.5000  67800.9000
## 75   60275.9000  66318.8000  67329.7000  54166.0000 110170.0000
## 76   12316.0000  39960.5000  39761.0000  50388.6000  58046.2000
## 77   89524.9000 104821.0000 104452.0000  79752.2000  29931.7000
## 78   27536.6000   7054.1600  36118.2000  49800.5000  36743.9000
## 79   24464.0000  14461.8000  34523.6000  28740.3000  26173.0000
## 80   73357.9000  47684.3000  27623.4000  26215.9000  68280.0000
## 81   56845.6000  36993.1000  33858.5000  42919.0000  29858.7000
## 82  100356.0000  86026.2000  63189.6000  57865.1000  45971.0000
## 83   43662.5000  20681.4000  36278.5000  99512.3000  48391.3000
## 84   39448.1000  57346.1000  60485.4000  29345.2000  12288.4000
## 85   18879.1000  51071.5000  49153.8000  23377.1000  26310.6000
## 86   15778.1000  11755.6000  16400.2000  22294.7000  14166.8000
## 87   16251.6000  10182.7000  19101.2000  18559.8000  17022.2000
## 88   47956.1000  27909.2000   8911.5800  15215.2000  25210.6000
## 89   30631.2000  23000.2000  11561.4000  38541.2000  26310.1000
## 90   39539.7000  29551.1000  40483.0000  57565.0000  33105.2000
## 91   70002.6000  65136.5000  50802.1000  27439.6000  30667.2000
## 92    8094.1800  21583.5000  19400.8000  20172.5000  17023.9000
## 93   19844.0000  20103.1000  24884.3000  18693.2000  24071.0000
## 94   40809.2000  24868.3000  28065.9000  30003.3000  29064.4000
## 95   37508.4000  36848.0000  47047.8000  33719.2000  14999.8000
## 96   20727.8000  35544.5000  27353.4000  11647.3000  17383.8000
## 97   23620.6000  31859.2000  19164.9000  30612.5000  28710.6000
## 98   25973.3000  26215.9000   9194.3800  12697.9000  14939.4000
## 99   29541.7000  18474.4000  15594.2000  37078.9000  24434.0000
## 100 104251.0000  74774.7000  68650.9000  38992.4000  55129.1000
## 101  44695.9000  40699.3000  33135.9000  43197.1000  56957.5000
## 102  64582.5000  57291.0000  61708.1000  41666.2000  59302.5000
## 103  22292.7000  34634.7000  36615.5000  71118.8000  40438.6000
## 104  75025.8000  41719.8000  27553.9000  31790.2000  28454.6000
## 105  21733.9000  25407.6000  36103.8000  30434.4000  21437.2000
## 106  66900.9000  47322.5000  43417.6000  59275.9000  51393.6000
## 107  27830.6000  21607.1000  21352.4000  19382.9000  21388.5000
## 108  34292.9000  31573.8000  72915.8000  51127.0000  53593.1000
## 109  25005.6000  20144.2000  18190.4000  21803.7000  18217.8000
## 110 109414.0000  80199.7000  89788.8000 129546.0000  41456.1000
## 111  15849.2000  17377.0000  17687.4000  20059.3000  17799.3000
## 112  34413.7000  35256.8000  44289.6000  30771.9000  38211.4000
## 113  41882.0000  41605.3000  61258.2000  32903.3000  41114.6000
## 114  88741.4000  64805.4000  35609.2000  53640.4000  42681.9000
## 115  43938.5000  76802.8000  27127.7000  30825.6000  36136.0000
## 116  42206.9000  39344.2000  41882.2000  35219.5000  26812.8000
## 117  20686.4000  15492.8000  15190.3000  25582.6000  22923.7000
## 118  78124.0000  49085.6000  65529.8000  30282.5000  32269.9000
## 119 344787.0000  44986.2000  68612.2000  87907.8000 126505.0000
## 120  35246.8000  85990.2000  66493.8000  81445.3000  78569.9000
## 121  27255.7000  24276.5000  92657.7000  71682.6000 101827.0000
## 122  57964.5000  92108.5000  90910.9000  59168.4000  74221.5000
## 123  33462.2000  31007.1000  31084.0000  56751.4000  39723.7000
## 124  35867.5000  48887.3000  26576.2000  24777.6000  33355.5000
## 125  19977.8000    914.9260  47593.1000  33142.0000  36106.4000
## 126  27839.1000  42043.6000  28878.9000  15634.3000  18241.1000
## 127  60268.5000  66534.5000  47596.4000  39799.4000  20638.6000
## 128  35987.1000  33252.1000  26796.5000  17896.7000  19446.3000
## 129  25600.9000  15624.8000  26705.7000  32701.3000  19357.3000
## 130  14876.7000  37259.3000  14206.1000  24054.0000  88750.3000
## 131  26827.0000  10588.5000  16555.6000  13541.5000  13476.2000
## 132  11790.5000  14637.9000    819.2300  17693.7000   8036.4000
## 133  87530.9000  40121.2000  17322.4000  39045.0000  31910.8000
## 134  10941.4000  62105.3000  28772.6000  43215.9000  38591.5000
## 135  47821.9000  70905.6000  85857.4000  70232.8000  54024.9000
## 136  41687.0000  75451.3000  36528.7000  22926.2000  36155.8000
## 137  36019.6000  32821.3000  34139.8000  15624.8000  33186.3000
## 138  22686.9000  21544.9000  17092.9000  47567.6000  32112.4000
## 139   1041.6500  25987.0000 361065.0000  21334.0000  21716.6000
## 140  14583.2000  32139.4000  41181.0000  21951.3000  27353.4000
## 141  27851.2000   9409.5700  28235.9000  38835.2000  26002.2000
## 142  13421.2000  70126.9000  28778.3000  12458.2000  26796.5000
## 143  20281.7000  14619.6000  28828.5000  36490.4000   5120.1900
## 144  63055.8000  55688.4000  53057.1000  59361.4000  55901.5000
## 145  45057.6000  59214.1000  33017.6000  57291.0000  19799.0000
## 146 105174.0000 135441.0000  92251.6000  83642.7000  98307.6000
## 147  26153.9000  21938.3000  26751.1000  22813.3000  59632.1000
## 148  60609.5000  18221.6000  31463.0000  24845.5000  24095.0000
## 149  69406.8000  56560.8000  93001.6000  55750.1000  44203.3000
## 150 102343.0000  25724.7000  67121.0000  75030.3000  57891.3000
## 151  46354.5000  31543.6000  37683.1000  16128.4000  32155.8000
## 152  71993.6000  48633.3000  65648.2000  61971.1000  36228.9000
## 153  64163.6000  55210.2000  58967.2000  57008.6000  45178.2000
## 154  29060.8000  44905.2000  22174.1000  14270.1000  29246.2000
## 155  27835.6000  21261.2000  98825.6000  19633.6000  15557.1000
## 156  26456.3000  29266.5000  13601.4000  30721.1000  35200.8000
## 157  27316.2000  34443.6000  36567.1000  25115.6000  24885.0000
## 158  39167.8000  31387.6000  36875.8000  37201.5000  18527.3000
## 159  10624.9000  32463.3000  32298.2000  29809.2000  24028.7000
## 160  41663.3000  18477.3000  12708.4000  17291.5000   7680.2800
## 161  27339.4000  24697.2000   2178.9500  22039.8000  14664.4000
## 162  35982.9000  25329.6000  28124.7000  21066.1000  28441.7000
## 163  93695.6000  88482.4000  21866.8000  42548.9000  25109.8000
## 164  13184.3000  22537.9000  28379.9000  28459.7000  21617.3000
## 165  16802.4000  35012.4000  37064.4000  35764.1000  48937.0000
## 166  66745.3000  71110.7000  50378.6000  98472.2000  74674.3000
## 167 157833.0000 196762.0000  35318.4000  36935.2000  39825.4000
## 168  14175.7000  20450.8000  77033.7000  54664.9000   6249.9200
## 169  49175.5000  56919.5000  53742.7000  49451.9000  59083.3000
## 170  50433.1000  58638.9000  51470.1000  44730.1000  37515.1000
## 171  33219.9000  64917.5000  79085.0000  78321.6000  71682.6000
## 172  24501.5000  30635.8000  21273.1000  20894.8000  34567.0000
## 173  18221.6000  21759.3000  13069.2000  19998.2000  24994.8000
## 174  28540.4000  19262.1000  56434.5000  42809.2000  71385.8000
## 175  46326.6000  19293.5000  65547.6000  60472.9000  41538.8000
## 176  50629.8000  31818.2000  33775.1000  32251.8000  44600.3000
## 177  51990.3000  51069.6000  24999.7000  57538.9000  76539.2000
## 178  35434.2000  24196.7000  52403.9000  12243.9000  39582.9000
## 179  74763.8000  70374.3000 176857.0000 194698.0000  59843.3000
## 180  66562.4000  40045.6000  40337.0000  25492.5000  43902.6000
## 181  82060.2000  48736.3000  43967.3000  40104.6000  61360.6000
## 182  20270.8000  24713.6000  22445.6000   3276.9200  29388.3000
## 183  27060.9000  24920.2000   6413.1200  20460.8000  21962.7000
## 184  59690.7000  28573.1000  30147.0000  21026.0000  18947.3000
## 185  15386.6000   8627.9200  20104.9000   2606.8500  55857.8000
## 186  19181.2000  36386.4000  21437.2000  19892.6000  22768.0000
## 187  26086.8000  19507.9000  26558.1000  26709.4000  14748.7000
## 188  30307.6000  39700.9000 107384.0000  77124.8000 212932.0000
## 189  53723.9000  70745.2000  47029.4000 389134.0000  27353.4000
## 190  32155.8000  45341.3000  43963.2000  58551.4000  34299.6000
## 191  31632.7000  29547.2000  27290.7000  34006.6000  84482.3000
## 192  30912.5000  16297.3000  14336.5000  24813.1000  38601.4000
## 193  25250.7000  18235.7000  29165.7000   9821.4500  20910.7000
## 194  71792.0000  38060.0000 120355.0000   6645.5400  78124.0000
## 195  21040.2000  22188.3000  30603.5000  16412.0000  16077.9000
## 196  43062.3000 104165.0000  30176.6000  43831.3000  48848.6000
## 197  39562.4000  40595.5000  25165.1000  53385.4000  40483.0000
## 198  76403.1000  94359.8000  41625.2000  42055.4000  39388.9000
## 199  29244.3000  16077.9000  46782.3000  45464.8000  45148.8000
## 200  25906.4000  20221.8000  21873.5000  11296.4000  25251.9000
## 201  15751.6000  25210.6000  21437.2000  31637.8000  27422.5000
## 202  35449.8000  35102.7000  37396.7000  29333.7000  43228.6000
## 203  25461.3000  17522.6000  10941.4000  23057.5000  20243.1000
## 204  66383.8000  30959.3000  39800.7000  18723.3000  19203.8000
## 205  27138.4000  18012.1000  22316.4000  28374.4000  17778.1000
## 206  43757.7000  36550.4000  37499.5000  51697.4000  35715.6000
## 207  26133.9000   9173.5100  50360.5000  30065.8000  27349.8000
## 208  10542.5000  16077.9000  17866.5000  26470.1000  18772.2000
## 209  52648.0000  43624.0000  22117.0000  13522.4000   5042.1200
## 210  59741.5000  50543.3000  55595.8000  46476.9000  18228.9000
## 211  12807.0000  11092.7000  35389.6000  64020.5000  38549.8000
## 212  41977.5000  53736.9000  45826.8000  25210.6000  63788.0000
## 213  34028.6000  28075.6000  35351.4000  25055.6000  21827.0000
## 214  11799.5000   5931.7200  16249.8000   9529.3000   3129.5000
## 215  17306.2000  15936.5000  14106.1000  11246.7000  18299.1000
## 216  43679.7000  54462.6000  43070.4000  39079.5000  45180.3000
## 217  30252.8000  25169.0000  17588.1000  35615.5000  32499.8000
## 218  24946.7000   7291.5800 172570.0000  36095.1000  20913.9000
## 219  23451.0000  29023.4000  25999.1000  46371.6000  59260.8000
## 220  51260.8000  78501.9000  97342.7000  67638.6000  59555.8000
## 221  83888.8000  78877.0000  45930.4000 176622.0000  45526.3000
## 222  82372.0000  42303.3000  62407.5000  65316.3000  53455.4000
## 223  10095.8000  96968.5000 100656.0000  69749.6000  87542.1000
## 224  64656.8000  57356.9000  59799.1000  41714.1000  56315.2000
## 225  50999.7000  46117.2000  26875.4000  54068.3000  19814.2000
## 226  77236.3000  61154.3000 127164.0000  98708.7000  75226.4000
## 227  45625.9000  57161.2000  40336.3000  41217.2000   9623.2600
## 228  68176.4000  64696.3000  55928.4000  57717.0000  66845.2000
## 229  72683.4000  84589.8000  46351.0000  36517.5000  19192.8000
## 230  55800.9000  99445.9000  60725.6000  35647.0000  53212.7000
## 231  77639.0000  89019.2000  56327.9000 184539.0000  67562.8000
## 232  41505.8000 104285.0000  52595.7000  40465.1000  54248.6000
## 233  39240.0000  86052.5000  71894.0000  55410.9000  63273.1000
## 234  40436.1000  24246.7000   7299.9600 172768.0000  37911.6000
## 235  37411.0000  51546.9000  54131.8000  42315.6000 100126.0000
## 236  47705.9000  49603.2000  54711.2000  57182.8000  59618.7000
## 237  56070.8000  44537.0000  45428.4000  47279.2000  53894.7000
## 238  51375.3000 157745.0000  13016.4000  43653.7000  68896.9000
## 239  41033.0000  30854.4000  53295.1000  48099.1000  13854.8000
## 240  55073.1000  43302.3000  19078.1000  83701.3000  62534.8000
## 241  40258.7000  58509.0000  41774.0000  44055.6000 103335.0000
## 242  74727.2000  50720.5000  48143.2000  56484.0000  62940.5000
## 243  69871.1000  59798.6000  70912.5000  52095.1000  79446.3000
## 244  20857.0000  82969.6000  46606.6000  51357.7000  58866.5000
## 245  65207.9000  48958.5000  49775.8000  30037.9000  47726.9000
## 246  40575.3000  20712.1000  20216.3000  22506.2000   7517.5500
## 247  43816.0000  75326.8000  61066.1000  61512.9000  75944.8000
## 248  77119.1000  43996.9000  24343.9000  75717.2000  67396.5000
## 249  38944.2000  20025.3000  35298.3000  78872.1000  82365.4000
## 250  47408.3000  86266.4000  61892.0000  57961.7000  62805.1000
## 251  64656.8000  95926.1000  86264.9000  88666.8000  50995.0000
## 252  59392.2000  49222.3000  35803.8000  43059.0000  76704.4000
## 253  52111.9000  68817.2000  44469.7000  72429.8000  65790.4000
## 254  67030.0000  51357.0000  49647.0000  27789.7000  93336.8000
## 255  53042.3000  90451.2000  86324.0000  75435.4000  55800.9000
## 256  80101.1000  71538.2000  32321.4000 133726.0000  87910.7000
## 257  63990.9000  33532.7000  78441.2000  74619.8000  69992.3000
## 258 105496.0000  30756.4000  97645.2000  55780.7000  88302.2000
## 259 145999.0000 107309.0000  87753.6000  62842.1000  50479.2000
## 260  44902.3000  46756.3000  52775.8000  50645.6000  42402.6000
## 261  46215.1000  21605.1000  21605.1000  63707.3000  18916.8000
## 262  70522.5000  87110.0000  76879.4000  47999.9000  71232.4000
## 263  46501.2000  91252.7000  67428.7000  55256.1000  49486.9000
## 264  54339.3000  56650.6000  77123.9000 100991.0000  49732.4000
## 265  72966.5000  72224.0000  41295.7000  68651.8000  58797.5000
## 266  38894.8000  28613.0000  47385.7000  14257.1000  44512.2000
## 267  48289.2000  45056.9000   1025.2100  19792.0000  26370.4000
## 268  28101.7000  35830.5000  41898.7000  38779.2000  52845.2000
## 269  33288.6000  27751.2000 196841.0000  51097.5000  27287.1000
## 270  41765.4000  37100.2000  51260.8000  33967.5000  42922.9000
## 271  44998.6000  20800.7000  15467.5000  11414.4000  53468.3000
## 272 143107.0000  48110.6000  41161.7000  19827.5000 119561.0000
## 273  41351.6000  65817.3000  30738.0000  54163.8000  46309.6000
## 274  21512.1000  31068.5000  16393.6000  21602.1000  18697.6000
## 275  36215.7000  38494.6000  44805.6000  43780.7000  58524.9000
## 276  40903.4000  38504.7000  33117.2000  32834.3000  29208.2000
## 277  48289.2000   1436.6800  42087.7000  36700.1000  37542.7000
## 278   4830.1600  17964.2000  22870.0000  26603.4000  16740.3000
## 279  14425.2000  32192.8000  43080.6000  66882.7000  46372.5000
## 280  32712.6000  30365.2000  33398.4000  49210.3000  23760.7000
## 281  70139.5000  11277.4000  21780.7000  32486.8000  28787.4000
## 282   5126.0800  23888.8000  26857.4000  15639.1000  30087.9000
## 283   5580.0900  32192.8000  74967.5000  61038.2000  25095.4000
## 284  85063.1000  25239.6000  28405.3000  44058.6000  31308.0000
## 285  46798.6000  56243.7000  36499.8000  38925.6000  53097.2000
## 286  26157.4000   9043.0900  18127.6000  26422.6000  39538.4000
## 287  59741.6000  55442.3000  80154.3000  32282.8000  63614.0000
## 288  45771.0000  30787.0000  53300.9000  35188.6000  29320.5000
## 289  88134.2000  50063.2000  73758.0000  85263.5000 206409.0000
## 290  46517.1000  76896.0000  87981.3000 105020.0000  78370.0000
## 291  54390.4000  69379.5000  15019.0000  65559.7000  79333.6000
## 292  65322.5000  38757.6000  51488.8000  51220.2000  60575.1000
## 293  42139.7000  43182.1000  50233.4000  39267.7000  37357.9000
## 294  68963.9000  65007.3000  56136.6000  66665.7000  54320.0000
## 295  25031.6000  41039.5000   6257.1100  92084.0000  91271.4000
## 296  49031.1000  52796.2000  39271.1000  30410.2000  42972.5000
## 297  47952.8000  45718.1000  37223.2000  45394.4000  44937.2000
## 298  26592.7000  39279.0000  23193.8000  49469.7000  29482.1000
## 299  31086.6000  38426.7000  32720.5000  32592.3000  31119.7000
## 300  66922.5000  50772.0000  39162.6000  53052.9000  30909.1000
## 301  23545.0000  23596.2000  28073.1000  26363.9000  20351.2000
## 302  31234.0000  34311.7000  25963.5000  24789.5000  26782.6000
## 303  24255.0000  32294.5000  37558.3000  26719.3000  22882.5000
## 304  26249.5000  26765.5000  26798.0000  26651.5000  48289.2000
## 305  16667.2000  12877.1000  21278.2000  30444.7000  32192.8000
## 306  24010.1000  27539.9000  21818.8000  22034.3000  50972.3000
## 307  55527.2000 100471.0000  66529.2000  76274.8000  86242.9000
## 308  86482.0000  65591.8000  56245.8000  91742.0000  59235.5000
## 309  34274.5000  34339.0000  36805.5000  35653.1000  67464.6000
## 310  23220.5000  32192.8000  60589.5000  63592.8000  48679.5000
## 311  56446.2000  44734.8000  40048.2000  46746.9000  79796.3000
## 312  24646.4000  11295.6000   7240.7600  11105.4000   9226.9300
## 313  15143.2000  19361.8000  27258.8000   2885.6500   9086.2600
## 314  36814.2000  27225.5000  28147.5000  29704.6000  20932.4000
## 315  17195.7000  11058.1000  22310.2000  13354.5000  18111.3000
## 316  14759.8000  13432.5000  24829.5000  24716.1000  25754.3000
## 317   9079.5200  22065.7000   8692.0600  10999.6000  12582.5000
## 318  15744.0000  14208.9000  13060.9000  18559.8000  12115.0000
## 319  16084.5000  11667.6000  12850.5000   1312.4600  14762.5000
## 320  14286.2000   9248.0300  11976.3000  16750.2000  10502.2000
## 321  29964.4000  29449.7000  32328.4000  26287.5000  33688.6000
## 322  17567.7000  39817.8000  25630.4000  20522.1000  18304.6000
## 323  18696.4000  16394.3000  18450.9000  19554.7000  35814.1000
## 324  19720.3000  19320.4000  19281.3000  19819.3000  22114.0000
## 325  27626.3000  39704.5000  50077.9000  23437.8000  43703.3000
## 326   4292.3800  25031.6000  15813.9000  18803.2000  38590.2000
## 327   7338.7500  16268.5000  13065.0000   2624.9000  15902.1000
## 328  49710.6000  30961.5000  25620.0000  11584.6000  12115.0000
## 329  30721.3000  22996.4000   4505.6900  19664.7000  18239.0000
## 330  23431.9000  21409.4000  45069.9000  15470.3000  21714.2000
## 331  13295.7000  17689.2000   3923.5300   9530.5900  10568.6000
## 332  34143.3000  14141.4000  25580.5000  20863.3000   5531.7600
## 333  20330.3000   3656.8400  18433.1000  15622.2000  22825.8000
## 334  30244.6000  39860.9000  26902.8000  38050.2000  37770.3000
## 335  73830.0000  14374.4000  12975.6000  10657.2000   9719.5000
## 336  23755.5000  29488.1000  35251.1000  22383.3000  32306.7000
## 337  17109.0000  22036.5000   7139.2200  21682.0000  18546.4000
## 338  62197.7000  42315.9000  60254.1000  43123.1000  47333.1000
## 339  95791.8000  35093.1000  35099.6000  42402.6000  34522.1000
## 340  68038.3000  58082.6000  71307.3000  44195.6000 164948.0000
## 341  58215.6000   3921.2800  68104.1000  56926.4000  32279.2000
## 342  97275.7000 111057.0000  22399.7000  17346.1000  24705.7000
## 343  29159.7000  17827.8000  48220.4000  29384.6000  32781.1000
## 344  30287.6000  53576.6000  37557.9000  37228.1000  25346.1000
## 345  29658.7000  26706.3000  48587.7000  42033.8000  35457.0000
## 346  24001.3000  30509.6000  37095.0000  22942.7000  12877.1000
## 347  42367.7000  34587.5000  40777.6000  28339.0000  30010.6000
## 348  21567.7000  44529.4000  64418.3000  47648.4000  45056.9000
## 349  21690.9000  36499.8000  22345.0000  25294.3000  53185.1000
## 350  43059.0000  36565.3000  31035.9000  27246.1000  20022.8000
## 351  24301.3000  22718.6000  23309.1000  28090.7000  29271.2000
## 352  25555.1000  26495.3000  78034.1000  18108.3000  21807.7000
## 353  17599.1000  14483.5000  27241.8000  27693.4000  24605.2000
## 354  18171.5000  10628.0000  41349.8000  45694.9000  49411.4000
## 355  23998.6000  25471.9000  33966.7000   2002.5300  40144.5000
## 356  19810.7000  41126.6000  28890.7000  27900.4000  44344.8000
## 357  28496.0000  24884.8000  23803.2000  21940.2000  52116.9000
## 358  31198.9000  24226.5000  33457.7000  31233.2000  38410.9000
## 359  54029.7000  49196.8000  65082.2000  56198.6000  57335.2000
## 360  58493.1000  52026.8000  44060.2000  37558.3000  41046.8000
## 361  27403.1000  25466.3000  32532.5000  15535.3000  26483.2000
## 362  18575.5000  15066.5000  19539.8000  16470.4000  27625.1000
## 363  31413.3000  31085.0000  27323.0000  27372.8000  31480.9000
## 364  42842.0000  26603.5000  30376.3000  26107.5000  19477.8000
## 365  26033.1000  36434.8000  28788.9000  45775.2000  61512.9000
## 366  17140.5000  24760.4000  27343.1000  27322.3000  30884.1000
## 367  19560.3000  16096.4000  30741.8000  11512.6000  15133.2000
## 368  30399.1000  47280.5000  15642.8000  31977.9000  29641.4000
## 369  42005.0000  23222.1000  85513.9000  30037.9000  16482.3000
## 370  16192.5000  25553.8000  15961.2000  20142.7000  38446.3000
## 371  25239.6000  35036.3000  40221.3000  48185.1000  29040.5000
## 372  41818.9000  42906.6000  51283.4000  66639.0000  29998.5000
## 373  30330.4000  18261.7000  49255.0000  34086.5000  27813.9000
## 374  23606.8000  18699.0000  31191.8000  28660.3000  82628.2000
## 375  15447.7000  25915.6000  74108.5000  36238.1000  23373.3000
## 376  38234.0000  40715.8000  45056.9000  33368.9000  26312.1000
## 377  26490.9000  27194.4000  15378.2000  30756.4000  36011.5000
## 378  31083.8000  57412.0000  33768.6000  26251.1000  26076.9000
## 379  22787.0000  21367.0000  34750.9000  30275.1000  33879.5000
## 380  21461.9000  29335.3000  34609.2000  64385.6000  23721.4000
## 381  40248.6000  61077.1000  46010.9000  43592.0000  55832.5000
## 382  10730.9000  29007.1000  23522.3000  25654.2000  29859.5000
## 383  45120.7000  55800.9000  37542.1000  42571.3000  20999.4000
## 384  39719.6000 161534.0000  47157.0000  85814.7000 200253.0000
## 385  43096.4000 120152.0000  43987.5000  44522.7000  49936.0000
## 386  30265.2000  47875.4000  42873.0000  61926.0000  66639.0000
## 387  34470.6000  34952.6000  33565.3000  22946.3000  20793.4000
## 388  45353.1000  54125.3000  25396.6000  38740.8000  37368.3000
## 389  68086.0000  37558.3000  47189.5000  43475.5000  46048.0000
## 390  28999.6000  43230.3000  34373.0000  28518.9000  34486.6000
## 391  40117.7000  35852.3000  46217.3000  16020.2000  34230.6000
## 392  31049.0000  28970.7000  37542.7000   4005.0600  29378.9000
## 393  24682.5000  35257.4000  47050.0000  39599.3000  37673.1000
## 394  40902.4000  21461.9000  23698.7000  25956.4000  35055.1000
## 395  39191.6000  32119.8000  42851.7000  56641.9000  61372.4000
## 396  62325.4000  42606.6000  42174.2000  48395.5000  38069.5000
## 397  30147.3000  74359.6000  31343.0000  30794.8000  28237.6000
## 398  35457.0000  42181.3000  47885.7000  16278.9000  27710.8000
## 399  25096.7000  57668.9000  42291.6000  60356.4000  21529.5000
## 400  23644.5000  29373.8000  24585.4000  30276.0000  16099.4000
## 401  15642.8000  27276.5000  32451.9000  21165.9000  27189.0000
## 402  27927.6000  23816.0000  13694.3000  37933.0000  30915.1000
## 403  22890.4000  24979.8000  19096.8000  12210.9000  29949.9000
## 404  47760.6000  51275.3000  52286.0000  24083.9000  28973.5000
## 405  48220.0000  38542.6000  41623.9000  36804.8000  29098.9000
## 406  30263.0000  10252.2000  43045.5000  48160.6000  43674.2000
## 407  19404.9000  29683.7000  29100.6000  27969.1000  27571.0000
## 408  31219.2000  18998.1000  21328.4000  10252.2000  34474.1000
## 409  11466.9000  18924.6000  17905.4000  25630.4000  18242.6000
## 410  14659.4000  18662.6000  32323.2000  16096.4000  24988.8000
## 411  35535.0000  22317.0000  24242.1000   5365.4700   9388.0500
## 412  37046.8000  55527.2000  42043.2000  37046.8000  21469.3000
## 413  30855.3000  25630.4000  25838.2000  27583.0000  28268.4000
## 414  26794.7000  23228.9000  22502.4000  46630.2000  43540.6000
## 415  13730.4000  42278.0000  44677.4000  42154.7000  17941.3000
## 416  21577.2000  19201.6000   8201.7200  32095.2000  28298.2000
## 417  27999.6000  25579.9000  36579.5000  24230.0000  35737.5000
## 418  33156.0000  25090.5000  21724.8000  33961.5000  32207.9000
## 419  36985.8000  29108.4000  42172.6000  26561.9000  26942.1000
## 420  43955.5000  19315.7000  22908.6000  25997.5000  32441.5000
## 421  32234.4000  19889.2000  19315.7000  52554.1000  26827.3000
## 422  31260.5000  42857.9000  30886.2000  23620.5000  23925.2000
## 423 190240.0000   1001.2600  81154.8000  58918.8000  52018.0000
## 424  37602.4000  22666.8000  69510.5000  43960.8000  37742.7000
## 425  12815.8000  21102.9000  25227.5000  28629.0000  34831.3000
## 426  30468.3000  27192.7000  24373.2000  16403.4000  23855.6000
## 427  63547.0000  88843.5000  56608.2000  57637.6000  71765.0000
## 428  62953.1000  57562.9000  37529.9000  45885.5000  17740.3000
## 429  18769.8000  21377.4000  16627.6000  16751.7000   8746.3200
## 430  24362.3000  21849.8000  12877.1000  12821.7000  11105.4000
## 431  53649.0000  69751.1000  40856.5000  26544.8000   5109.9700
## 432  22629.5000  19094.4000  19411.2000   8575.0300   8201.7200
## 433  22711.9000  23210.1000  21911.4000  21817.0000  18524.3000
## 434  31285.6000  20857.0000  25995.0000  15448.8000  17635.2000
## 435  19391.7000  34241.6000  14600.1000  26827.3000  40259.4000
## 436 109308.0000  60337.9000  72999.6000  64308.2000  67047.8000
## 437  38819.7000  33371.3000  16500.2000  12542.0000  15040.4000
## 438  30424.8000  45430.0000  88936.8000  60078.4000  63766.7000
## 439  60826.0000 127793.0000 123282.0000  75596.7000  77178.0000
## 440  62918.2000  69582.5000  74184.7000  30227.6000  66890.9000
## 441  83053.6000  37483.6000  48422.1000  99220.4000  70866.1000
## 442  53674.6000  90141.1000  60998.1000  61150.2000  69463.8000
## 443  71889.4000  80271.0000 169836.0000  57536.1000  56839.4000
## 444  53962.5000  84534.2000  49897.9000 137284.0000  28212.5000
## 445  68923.0000  71032.2000  71810.0000  74127.8000  44083.4000
## 446  71770.9000  52895.9000  67689.0000  89762.5000  70925.4000
## 447 118946.0000  61885.0000 113687.0000  51908.5000 218212.0000
## 448  28704.2000  49214.1000  40597.3000  80511.8000  26817.5000
## 449  72957.6000  83141.0000  81488.7000  96728.4000  76987.7000
## 450  38092.1000  57432.5000  54809.8000  41996.8000  50495.7000
## 451  40556.6000  26450.1000  34321.0000  82712.9000  62527.1000
## 452  38218.9000  64417.8000  70974.1000   6864.1900  59448.1000
## 453  18874.4000  49443.3000  46295.1000  14949.6000  70751.7000
## 454  60146.7000  90988.4000  58666.4000  45995.3000  62415.5000
## 455  82902.6000  49816.5000  54452.6000  58090.6000  47882.0000
## 456  77862.8000  31022.2000  47654.1000  31540.5000  36443.1000
## 457  50120.0000  83954.4000  40204.0000  27144.1000  19761.3000
## 458  31680.9000  54526.6000  39618.8000  97154.8000  40556.6000
## 459  65250.4000  35265.0000  42241.2000  52490.3000  57527.7000
## 460  64998.5000  48307.1000  45187.0000  27603.2000  54282.3000
## 461  32102.3000  73731.7000  58280.4000  44920.7000  70754.0000
## 462  61179.5000 123048.0000  51790.2000  85621.1000  21120.6000
## 463  38050.0000  45049.3000 105987.0000  48606.4000 122831.0000
## 464  50193.1000  50828.3000  32242.8000  30346.5000  43426.2000
## 465  55018.4000  54932.5000  96502.0000  15572.5000  89202.2000
## 466  51503.7000  70150.1000  97839.1000  61894.6000  50695.8000
## 467  66523.7000  48257.3000  35175.3000  36804.3000  41524.9000
## 468  61252.0000  84722.4000  90960.3000  78522.6000  47168.9000
## 469  68498.9000  77786.4000  82730.7000  92152.4000  50024.5000
## 470  54638.9000  25954.2000  41350.3000  48994.6000  61542.4000
## 471 176727.0000  52394.6000  72648.6000  53737.5000  68658.3000
## 472  75607.5000  87423.6000  88210.7000  98889.0000  67832.9000
## 473  85730.0000  87654.3000  91187.8000  80301.6000  81113.3000
## 474  78263.0000  78198.4000  69010.6000  81113.3000  78398.4000
## 475  64047.8000  54289.5000  82948.0000  67481.0000  41447.5000
## 476  87648.6000  86179.9000 130497.0000 106866.0000  31145.1000
## 477  61636.2000  87697.6000  45944.0000 121743.0000 147844.0000
## 478  42578.5000  31139.2000  48668.0000  51547.0000  27001.2000
## 479  40775.9000  25542.9000  22306.2000  43825.2000  48577.4000
## 480  23513.9000  32299.2000  42241.2000  34441.4000  66692.3000
## 481  92698.9000 191442.0000  83509.4000  95077.8000  49105.4000
## 482  64378.1000  47305.0000  30227.6000  45476.7000  69662.2000
## 483  39977.0000  63901.0000  60191.9000  53721.4000  53068.4000
## 484  34654.7000  35782.0000 269757.0000  22374.7000  39702.5000
## 485  14231.2000  44616.2000  57214.4000  70721.6000  28682.0000
## 486  28294.8000  29489.4000  38608.7000  35565.8000  57422.6000
## 487  52773.6000  55486.3000  39676.1000  38140.2000  41878.5000
## 488  30227.6000  23174.5000  43838.9000  37006.2000  25597.7000
## 489  29877.0000  51908.5000  31132.1000  40892.8000  35329.3000
## 490  22894.3000  15661.1000  27623.1000  52274.4000  41290.8000
## 491  47909.3000  39470.9000  20492.3000 125798.0000  42739.5000
## 492  55086.5000  43930.7000  55701.6000  47151.0000  40035.4000
## 493  30819.3000  18513.9000  22003.7000  18999.3000  20328.6000
## 494  38239.5000  45025.6000  43551.7000  60495.4000  34241.1000
## 495  28735.3000  33348.9000  27879.7000  40268.3000  25215.9000
## 496  39957.9000  38017.1000   3022.7600  34281.4000  16464.3000
## 497  22469.0000  17029.2000  50608.2000  20790.2000  17179.3000
## 498  13113.4000  50379.4000  64485.6000  35736.1000  25794.3000
## 499  37961.6000  29961.4000  38153.2000  26359.2000  24179.8000
## 500  85035.8000  11419.9000  19560.2000  27800.1000  30367.0000
## 501   5190.8500  25878.3000  27452.5000   9530.4500  28396.1000
## 502  71755.1000  61806.0000  25439.6000  45679.5000  25954.2000
## 503  42803.7000  32556.0000  41526.8000  26848.6000  30445.5000
## 504  47185.2000  54097.8000  26790.3000  32956.1000  20195.6000
## 505  25637.5000  37848.1000  31718.6000  61340.5000  17523.1000
## 506  31770.8000  64417.8000  44750.5000  62848.6000 172133.0000
## 507  54148.9000  38790.4000  31147.5000 105152.0000 120911.0000
## 508  75617.1000  83228.0000 215572.0000 167285.0000  42812.4000
## 509  88528.8000 118948.0000  84965.8000  22961.4000  61125.4000
## 510  67090.5000  61894.4000  72824.2000  81779.1000  56296.6000
## 511  54982.9000  61340.5000  54706.1000  55170.0000  48979.0000
## 512  37950.8000  48708.2000  96691.1000  72591.8000  92895.8000
## 513  66105.3000  64022.3000  57312.8000 132799.0000 151276.0000
## 514  25347.9000  46459.3000   8060.7000   7041.6700  90838.7000
## 515  42241.2000  52781.9000  39738.9000  33337.0000  45592.9000
## 516  49469.7000  49755.9000  42288.3000  37925.1000  45811.3000
## 517  19079.6000  42656.9000  23329.0000  50094.8000  27246.2000
## 518  33138.9000  39337.4000  33360.3000  34104.0000  53779.4000
## 519  36707.5000  50346.9000  35358.0000  60727.4000  20379.4000
## 520  23300.7000  34424.9000  50728.7000  26620.9000  21902.5000
## 521  45183.9000  31076.3000  36191.0000  26120.3000  25865.4000
## 522  29865.6000  26134.4000  35681.4000  26109.4000  22063.8000
## 523  26285.2000  29110.5000  28233.6000  26576.0000  16598.1000
## 524  19196.0000  20769.6000  31299.3000  12091.1000  28212.1000
## 525  22199.0000  21912.7000  27046.4000  51189.9000  60830.1000
## 526 101741.0000  68931.5000  80443.5000  87554.5000  74923.8000
## 527  83603.0000  67737.2000  56459.2000  96888.4000  40303.5000
## 528  35038.8000  34282.2000  36899.6000  40009.6000  69474.8000
## 529  23513.9000  64015.6000  42241.2000  54347.3000  59006.8000
## 530  55917.4000  39900.2000  40552.8000  47871.9000  87664.4000
## 531  31301.3000  11470.7000  10255.4000  11245.8000   4665.1100
## 532  14456.7000  19875.2000  27603.2000  18136.6000   3003.8200
## 533  28317.8000  51908.5000  28821.5000  28870.0000  29665.1000
## 534  17892.4000  18316.3000  18080.8000  10736.3000  23159.0000
## 535  15255.8000  12091.1000  16122.2000  12636.9000  26721.2000
## 536  11744.9000   9043.9300  21259.9000  11169.3000  13214.9000
## 537  15899.7000  20196.3000  15080.2000  18705.3000  14160.5000
## 538  17163.3000  11908.8000  13140.0000   2053.2000  20278.3000
## 539  13931.2000   8517.3200  12711.0000  17349.6000   9816.3400
## 540  28943.8000  32736.9000  25732.7000  40469.3000  38779.2000
## 541  18364.3000  35567.1000  25954.2000  10188.2000  20299.4000
## 542  19640.0000  20283.4000  18530.7000  20602.9000  36532.4000
## 543  19006.2000  18365.8000  25145.3000  20999.4000  22400.3000
## 544  28669.0000  42915.7000  26465.9000  40972.7000  31943.7000
## 545  16542.2000  18426.8000  39541.7000  33243.1000  44039.6000
## 546   3325.0400   7778.5500   2561.6900  16004.2000  14604.2000
## 547  36419.0000  26469.8000  12207.8000  12268.1000  25201.9000
## 548  17129.0000  23212.7000   4562.6200  22149.3000  18512.9000
## 549  22485.1000  19665.0000  21378.6000  24409.5000  25053.9000
## 550   3857.6200   9200.3800  12796.4000  10833.5000   9450.3700
## 551  13812.0000  24218.5000  21273.8000   4637.2500  22974.7000
## 552   3054.3900  18637.0000  15507.6000  23436.9000   7786.2700
## 553  42018.3000  24282.3000  43849.3000  42862.6000  76344.2000
## 554  13340.4000  10846.7000   6458.9300  18017.6000  15859.6000
## 555  33123.3000  24701.2000  32714.9000  10771.5000  56453.7000
## 556  23908.2000  19157.1000  57179.2000  41897.8000  34452.4000
## 557  44662.1000  36306.8000  93053.7000  40589.9000 114199.0000
## 558  30142.2000  24887.0000  42024.1000  65171.9000  40831.0000
## 559 127594.0000  62776.9000  63843.4000  47602.7000  19700.2000
## 560  26604.8000  43069.9000  44574.1000  53290.6000  52077.7000
## 561  18191.2000  16706.4000  26608.5000  28258.2000  43187.9000
## 562  70531.2000  48352.1000  35487.1000  22742.2000  27768.6000
## 563  39811.0000  24749.7000  26068.6000   9888.6600  25021.2000
## 564  58440.1000  34201.9000  33132.8000  33104.4000   3244.5300
## 565  30586.5000  40129.1000  35907.0000  23522.7000  33833.6000
## 566  26580.9000  37983.1000  36917.9000  45077.1000  20971.1000
## 567  23696.8000  25705.8000  24434.6000  36961.1000  22321.6000
## 568  34819.6000  24334.0000  43603.1000  36433.5000  34011.1000
## 569  30417.5000  28297.1000  23975.6000  25433.1000  17025.3000
## 570  49991.0000  42241.2000  24610.8000  27263.7000  40464.8000
## 571  11998.5000  20730.9000  18098.5000  15462.2000  28739.2000
## 572  19865.5000  19141.0000  25347.9000  19960.9000  21471.1000
## 573  45623.6000  52522.4000  41255.5000  26509.0000  26872.7000
## 574  22250.1000  23851.9000  23717.1000  18895.5000  37344.4000
## 575  33877.4000  39734.1000  28268.8000  23816.2000  24160.6000
## 576  38288.3000  50379.4000  33054.0000  34135.5000  10945.3000
## 577  14912.7000   6681.9300  34833.0000  33351.7000  38992.0000
## 578  42374.7000  52024.5000  48634.1000  48668.0000  57117.6000
## 579  46129.5000  40213.6000  46813.6000  50023.8000  27367.5000
## 580  23662.9000  21729.3000  19903.7000  19546.2000  15827.0000
## 581  22491.5000  34203.6000  30670.3000  15335.1000  31686.3000
## 582  37958.3000  28736.1000  38955.3000  35564.4000  35076.3000
## 583  49681.9000  20763.4000  21474.4000  42232.7000  26046.8000
## 584  27691.0000  19762.4000  44641.3000  17578.7000  20479.0000
## 585  25230.2000  22013.5000  27399.4000  20679.6000  23283.7000
## 586  31967.5000  35550.1000  30655.1000  60042.8000  15840.5000
## 587  32585.0000  36033.7000  39852.9000  24091.6000  30227.6000
## 588  18198.4000  23593.3000  14909.2000  15894.2000  29482.7000
## 589  12458.0000  24325.5000   7690.8000  16046.1000  25558.6000
## 590  30301.9000  63923.7000  33983.9000  37430.0000  35674.1000
## 591  21956.6000  15845.5000  27392.1000  24482.7000  29869.3000
## 592  36993.4000  15048.0000  28628.8000  23608.1000  21422.3000
## 593  55417.3000  37519.2000  37790.1000  15943.4000  24286.8000
## 594  15625.5000  31258.1000  38852.3000  36431.2000  22672.5000
## 595  52934.9000  53451.1000  30275.6000  22639.2000  15447.7000
## 596  20312.5000  36375.4000  35585.5000  38518.0000  29347.3000
## 597  25863.9000  27856.2000  26357.0000  14796.5000  24035.8000
## 598  36218.9000   5190.8500  16849.6000  60455.3000  18142.4000
## 599  14756.2000  27192.8000  50171.7000  29767.4000  45439.6000
## 600  27365.7000  23280.8000  32856.0000  26090.6000  29323.6000
## 601  41446.7000  45204.1000  67481.0000  72546.3000  43770.5000
## 602  54570.9000  39400.8000 163575.0000  51309.0000  86899.1000
## 603  52390.8000  43490.2000 121670.0000  95609.1000  45085.3000
## 604  51317.4000  45791.4000  31018.6000  48837.8000  40893.0000
## 605  22447.9000  60455.3000  60455.3000  11252.3000  34575.8000
## 606   9165.0600  28829.6000  35011.9000  24013.5000  41526.8000
## 607  59389.8000  44923.6000  61936.4000  30833.6000  44760.6000
## 608  35828.5000  11627.5000  32036.3000  31126.3000  33142.3000
## 609  25772.4000  30732.0000  42477.1000  38830.6000  50592.1000
## 610  53263.0000  29352.5000  35155.7000  32207.7000  29303.2000
## 611    101.3920  20555.8000  25465.1000  36961.1000  50508.2000
## 612  15206.8000  35744.6000  31444.0000  32898.7000  34400.5000
## 613  43415.1000  43570.8000  43749.7000  37737.9000  34551.1000
## 614  43555.4000  38859.2000  38017.1000  86272.9000  40150.3000
## 615  32461.7000  23924.5000  65254.9000  31650.1000  84111.3000
## 616  37794.2000  25153.7000  38951.2000  34966.1000  35905.0000
## 617  31116.7000  24685.9000  35156.7000  19656.2000  38092.1000
## 618  56329.3000  48231.9000  27384.0000  23581.4000  29241.0000
## 619  26835.1000  23495.8000  37957.7000  15840.5000  27215.4000
## 620   9725.0000  12786.5000  20820.9000  29069.8000  25126.5000
## 621  49832.2000  23181.0000  25521.2000  23550.8000  24254.8000
## 622  22873.3000  43712.5000  33570.1000  54409.8000  47756.7000
## 623  26302.5000  31757.4000  30048.9000  45604.5000  31095.6000
## 624  39458.5000  11280.2000  50695.8000  30519.5000  10381.7000
## 625  33206.7000  29647.9000  30332.4000  26637.6000  29025.0000
## 626  18136.6000  29771.3000  29543.8000  28727.8000  31045.2000
## 627  20846.3000  16628.3000  18379.4000   5725.1200  18839.8000
## 628  19236.6000  29664.3000  15341.5000  21221.8000  13441.8000
## 629  20028.1000   7097.4100  40344.4000   2027.8300  30459.6000
## 630  30096.9000  24104.5000   9611.0600  18482.9000  14784.4000
## 631  12887.5000  39073.1000  23319.0000  26361.8000  25954.2000
## 632  86894.6000 100817.0000  72671.9000  70333.6000  47099.9000
## 633  56779.3000  58475.8000  51549.5000  34108.3000  50161.7000
## 634  18168.0000  33560.0000  23672.1000  26580.9000  26963.6000
## 635  24825.2000  33221.4000  19417.1000  17379.8000  32382.9000
## 636  37017.4000  42813.4000  33881.8000  21366.8000  22306.2000
## 637  29767.2000  40180.8000  26161.2000  29399.7000  22166.9000
## 638  24818.3000  40159.8000  29648.0000  21913.6000  22176.6000
## 639  36561.0000  25761.9000   3169.2600  33855.0000  32262.4000
## 640  24619.2000  22472.0000  14100.8000  29792.4000  32931.7000
## 641  37960.2000  48167.4000  33390.6000 120657.0000  93926.3000
## 642 124580.0000  38546.5000  66578.9000 114709.0000  47034.2000
## 643  25400.9000   8616.8100  23688.3000  27880.5000  12561.5000
## 644  30309.2000  26017.2000  26664.6000  36715.5000  33961.3000
## 645  18292.4000  23509.0000   8305.3600  75947.2000  67909.6000
## 646  67113.6000  31827.5000  60147.0000  60733.5000  54090.9000
## 647  15610.1000  13597.5000  23430.3000  18962.4000  19838.7000
## 648  40129.1000  57099.5000  64417.8000  25266.5000  21448.6000
## 649  20465.0000  48701.9000  45365.3000  36961.1000  41222.6000
## 650  38776.3000  30764.6000  22345.6000  20173.3000  19603.6000
## 651  21716.1000  13817.2000  19461.6000  22421.3000   6906.4600
## 652  17892.3000  16569.7000  16510.0000  21120.6000  26727.3000
## 653   1557.2600  32672.8000  22891.3000  31635.4000  20059.7000
## 654  49633.4000  38849.0000  65168.7000  64537.4000  69915.1000
## 655  52801.5000  33793.0000  18097.2000  10900.8000  13675.3000
## 656  23506.0000  43111.0000                                    
##             Nov         Dec
## 1    26041.3000  67608.9000
## 2    81923.0000  27905.0000
## 3   100842.0000  28418.6000
## 4   136108.0000  20226.3000
## 5    78688.6000  55689.0000
## 6   113560.0000  97502.7000
## 7     8499.5100  20686.9000
## 8     5573.6800  32824.1000
## 9    69671.0000  66425.7000
## 10   48233.7000  84248.5000
## 11   11264.4000  25311.6000
## 12   23891.8000  16477.8000
## 13   28124.7000  34415.1000
## 14   36847.1000  22532.8000
## 15   34299.6000  36556.0000
## 16   10077.1000  10217.6000
## 17   32752.2000  36196.7000
## 18   28097.7000  29580.5000
## 19   76397.5000  72095.7000
## 20   40730.7000  28921.6000
## 21   68908.1000  31576.6000
## 22   33465.4000  47161.9000
## 23   54995.0000  43800.5000
## 24   70642.1000  32269.6000
## 25   22633.8000  30889.7000
## 26   14651.2000  12937.7000
## 27   33888.8000  24652.1000
## 28   44548.7000  41573.5000
## 29   96550.0000 102796.0000
## 30   10881.4000  11673.5000
## 31   78124.0000 166559.0000
## 32    7082.9800   8192.3000
## 33   44764.3000  46306.0000
## 34   14515.1000  13430.2000
## 35   85769.5000  53026.4000
## 36   50196.2000  57480.8000
## 37   95665.5000 104634.0000
## 38   80542.0000  18632.0000
## 39   42906.6000  27898.0000
## 40   22610.6000  41332.8000
## 41   37889.4000  37012.2000
## 42   35091.7000  22448.0000
## 43   22645.9000  71965.1000
## 44   34267.7000  25587.8000
## 45   26327.0000  22100.1000
## 46   10790.1000  34957.1000
## 47    6997.2900   9494.5800
## 48   14786.6000  22492.5000
## 49   29333.8000  46247.9000
## 50    3958.2900  33640.3000
## 51   28348.7000  25418.8000
## 52   16222.1000  27255.1000
## 53   25619.0000  32261.2000
## 54  160879.0000 114335.0000
## 55   35985.5000  66964.3000
## 56   42975.4000  43281.5000
## 57   11362.1000  12644.0000
## 58   31719.3000  24584.8000
## 59   53984.2000  45507.8000
## 60   39034.2000  33643.7000
## 61   49356.0000 109414.0000
## 62   60328.1000  47157.8000
## 63   46660.2000 123330.0000
## 64   24000.8000  24776.0000
## 65   32916.3000   5208.2700
## 66   35776.2000  40699.5000
## 67   24540.8000  10704.5000
## 68   35237.6000  46198.3000
## 69   46203.4000  21941.2000
## 70   43054.1000  24959.7000
## 71   36809.7000  30323.3000
## 72   25713.9000  25874.5000
## 73   52438.1000 101924.0000
## 74   59788.3000  65491.8000
## 75   57255.6000  65129.3000
## 76   58332.6000  56257.7000
## 77   21967.1000  24389.1000
## 78   33451.6000  27484.4000
## 79   57238.8000  50711.8000
## 80   27721.1000  65213.6000
## 81   39658.9000  41613.5000
## 82   31224.8000  21504.8000
## 83   38099.0000  29721.3000
## 84   43373.6000  37370.1000
## 85   21998.9000  25210.6000
## 86   15283.3000  31991.8000
## 87   46854.1000  36720.7000
## 88   34830.1000  58009.2000
## 89   14390.1000  27353.4000
## 90    3470.3200  91408.7000
## 91   19379.8000  54426.6000
## 92   22013.6000  19168.2000
## 93   25624.7000  34539.3000
## 94   24987.6000  22484.7000
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## 655  27798.7000  47600.3000
## 656
ddata4 <- decompose(tsdata4, "multiplicative")
plot(ddata4)

plot(ddata4$seasonal)

plot(ddata4$trend)

plot(allrace$avg_wage,type ="l",main = "Average salary for all races",xlab= "Year",ylab="Average Salary")

#class

class(tsdata4)
## [1] "ts"
mymodel4 <- auto.arima(tsdata4)
mymodel4
## Series: tsdata4 
## ARIMA(3,1,2)(1,0,1)[12] 
## 
## Coefficients:
## Warning in sqrt(diag(x$var.coef)): NaNs produced
##          ar1     ar2     ar3      ma1     ma2     sar1   sma1
##       0.6770  0.0438  0.0287  -1.4511  0.4616  -0.2754  0.265
## s.e.  0.0871  0.0222  0.0220   0.0867  0.0839      NaN    NaN
## 
## sigma^2 estimated as 710058227:  log likelihood=-91316.81
## AIC=182649.6   AICc=182649.6   BIC=182705.4
auto.arima(tsdata4,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,1,2)(1,0,1)[12] with drift         : 182708.3
##  ARIMA(0,1,0)            with drift         : 186329
##  ARIMA(1,1,0)(1,0,0)[12] with drift         : 184340
##  ARIMA(0,1,1)(0,0,1)[12] with drift         : 182893.8
##  ARIMA(0,1,0)                               : 186327
##  ARIMA(2,1,2)(0,0,1)[12] with drift         : 182635.2
##  ARIMA(2,1,2)            with drift         : 182633.7
##  ARIMA(2,1,2)(1,0,0)[12] with drift         : 182709.5
##  ARIMA(1,1,2)            with drift         : 182669
##  ARIMA(2,1,1)            with drift         : 182716.4
##  ARIMA(3,1,2)            with drift         : 182633.6
##  ARIMA(3,1,2)(1,0,0)[12] with drift         : 182616.1
##  ARIMA(3,1,2)(2,0,0)[12] with drift         : 182706.9
##  ARIMA(3,1,2)(1,0,1)[12] with drift         : 182614.6
##  ARIMA(3,1,2)(0,0,1)[12] with drift         : 182635.2
##  ARIMA(3,1,2)(2,0,1)[12] with drift         : 182698.3
##  ARIMA(3,1,2)(1,0,2)[12] with drift         : 182615.4
##  ARIMA(3,1,2)(0,0,2)[12] with drift         : 182636.3
##  ARIMA(3,1,2)(2,0,2)[12] with drift         : 182694.9
##  ARIMA(3,1,1)(1,0,1)[12] with drift         : 182641.3
##  ARIMA(4,1,2)(1,0,1)[12] with drift         : 182651.3
##  ARIMA(3,1,3)(1,0,1)[12] with drift         : 182617
##  ARIMA(2,1,1)(1,0,1)[12] with drift         : 182722.2
##  ARIMA(2,1,3)(1,0,1)[12] with drift         : 182720.1
##  ARIMA(4,1,1)(1,0,1)[12] with drift         : 182658.3
##  ARIMA(4,1,3)(1,0,1)[12] with drift         : 182647.6
##  ARIMA(3,1,2)(1,0,1)[12]                    : 182613
##  ARIMA(3,1,2)(0,0,1)[12]                    : 182633.2
##  ARIMA(3,1,2)(1,0,0)[12]                    : 182614.3
##  ARIMA(3,1,2)(2,0,1)[12]                    : 182696.4
##  ARIMA(3,1,2)(1,0,2)[12]                    : 182613.5
##  ARIMA(3,1,2)                               : 182631.8
##  ARIMA(3,1,2)(0,0,2)[12]                    : 182634.4
##  ARIMA(3,1,2)(2,0,0)[12]                    : 182705
##  ARIMA(3,1,2)(2,0,2)[12]                    : 182693
##  ARIMA(2,1,2)(1,0,1)[12]                    : 182706.3
##  ARIMA(3,1,1)(1,0,1)[12]                    : 182638.7
##  ARIMA(4,1,2)(1,0,1)[12]                    : 182648.9
##  ARIMA(3,1,3)(1,0,1)[12]                    : 182615.2
##  ARIMA(2,1,1)(1,0,1)[12]                    : 182719.4
##  ARIMA(2,1,3)(1,0,1)[12]                    : 182716
##  ARIMA(4,1,1)(1,0,1)[12]                    : 182656.3
##  ARIMA(4,1,3)(1,0,1)[12]                    : 182645.6
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(3,1,2)(1,0,1)[12]                    : 182649.6
## 
##  Best model: ARIMA(3,1,2)(1,0,1)[12]
## Series: tsdata4 
## ARIMA(3,1,2)(1,0,1)[12] 
## 
## Coefficients:
## Warning in sqrt(diag(x$var.coef)): NaNs produced
##          ar1     ar2     ar3      ma1     ma2     sar1   sma1
##       0.6770  0.0438  0.0287  -1.4511  0.4616  -0.2754  0.265
## s.e.  0.0871  0.0222  0.0220   0.0867  0.0839      NaN    NaN
## 
## sigma^2 estimated as 710058227:  log likelihood=-91316.81
## AIC=182649.6   AICc=182649.6   BIC=182705.4

residuals

library(tseries)
adf.test(mymodel4$residuals)
## Warning in adf.test(mymodel4$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel4$residuals
## Dickey-Fuller = -18.516, Lag order = 19, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel4$residuals)

acf(ts(mymodel4$residuals),main = "ACF Residual")

pacf(ts(mymodel4$residuals),main = "PACF Residual")

#forecasting for all races and mean

myforecast4 <- forecast(mymodel4, level =c (95), h= 3*12)
plot(myforecast4,main = "Salary forecasting for all races in next 3years",xlab = "Time", ylab ="Average salary")

myforecast4[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 656                                                               
## 657 36959.67 37028.49 36784.33 35961.21 35761.24 36274.87 35969.23
## 658 35830.88 35742.86 35756.18 35940.83 35963.08 35795.99 35860.20
## 659 35843.35 35864.08 35857.67 35804.66 35796.86 35841.59 35822.88
##          Aug      Sep      Oct      Nov      Dec
## 656 39905.47 38711.00 38260.37 37600.15 37053.08
## 657 35888.92 35844.97 35820.60 35857.96 35893.62
## 658 35866.72 35866.66 35863.88 35846.19 35830.58
## 659

forecasting for race 1: White

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
wrace <- subset(newrace, race_name == "White",select = c(avg_wage, year,race_name))
head(wrace)
wrace$avg_wage <- as.numeric(wrace$avg_wage)
wrace$Date <- paste (wrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata5 <- ts(wrace$avg_wage,frequency = 12)
tsdata5
##           Jan       Feb       Mar       Apr       May       Jun       Jul
## 1    66809.00  32865.60 160712.00  41850.10  62279.30  80406.10  56667.70
## 2    78688.60  49086.00 113560.00  56078.70  26407.60  32023.50  30002.70
## 3    46748.30 107549.00  37840.80  91407.80  65354.10  18360.80  36797.40
## 4    22047.00  25169.60  24499.90  31737.80  30693.70  55755.30  36748.80
## 5    48242.50  70699.50  39622.50  24633.20  54995.00  28061.40  70642.10
## 6    28131.50  33888.80  96495.20  44548.70  44362.40  68532.30 133997.00
## 7    43785.60  46306.00  27067.70  14515.10  71908.60  85769.50  51115.80
## 8    82468.50  37260.70  75928.90  51015.20  76178.30  40049.30  64392.60
## 9    34267.70  17572.50  28573.90  62135.00  38133.70  27029.60   8531.65
## 10   65387.80  11067.70  33640.30  28867.20  22423.40  22786.70  27255.10
## 11   46485.30  66964.30  48036.90  43281.50  32898.60  11362.10  33480.00
## 12   37909.40  92336.10  49350.80  65838.00  35975.40  89997.20  46660.20
## 13   42004.50  72093.70  42509.40  24540.80  60550.00  46409.20  39981.00
## 14   43032.90  30932.60  31903.50  56124.50  31890.70  25713.90  81099.50
## 15   52192.20  50388.60  34759.40  89524.90  29931.70  55818.50  36743.90
## 16   74828.50  57865.10  49156.20  48391.30  39448.10  43373.60  18879.10
## 17   46854.10  27909.20  34830.10  58009.20  39492.00  23000.20  26310.10
## 18   16525.20  21583.50  23362.50  24884.30  34539.30  31536.70  24987.60
## 19   30612.50  32326.80   9194.38  18601.50  37078.90  75589.90  55129.10
## 20   79270.10  41719.80  29859.70  36103.80  63982.90  59275.90  25018.20
## 21  135886.00  41456.10  20299.30  17799.30  37489.20  16037.70  41882.00
## 22   28896.10  29595.80  39344.20  15797.50  25582.60  36729.30  35087.20
## 23   92657.70  71247.00  81844.10  26831.30  33462.20  82880.00  28389.60
## 24   82654.70  47596.40  48614.00  35987.10  17896.70  27038.00  41659.00
## 25   52831.70  32753.90  32986.90  48236.40  43215.90  81584.90  38754.30
## 26   47567.60  28429.00  54385.30  20228.50  32139.40  27318.20  25737.90
## 27   16697.80  28828.50  99752.80  63055.80  40924.80  60989.80  19799.00
## 28   27269.60  26455.10  69406.80  42966.10 102343.00  57891.30  46354.50
## 29   32109.00  44308.00  22429.30  16765.60  26456.30  35203.80  36567.10
## 30   18477.30  20276.80 117913.00  35982.90 156341.00  98904.90  21866.80
## 31   71110.70 201876.00  35318.40  25985.20  77033.70  58064.70  53742.70
## 32   25308.40  21273.10  20521.50  24994.80  28643.90  56434.50  44212.70
## 33   62577.00  76539.20  58013.70  52403.90  49603.00  74763.80  74119.00
## 34   24713.60  43698.80  27060.90  57877.50  28573.10  39209.60  11148.50
## 35   58148.60 107384.00  60076.90  70745.20  23091.70  45341.30  31718.60
## 36   71792.00  28485.30  30603.50  33100.50  66003.80  32816.50  42171.10
## 37   20221.80  33890.30  31637.80  42896.40  49165.00  37396.70  46833.40
## 38   18012.10  57922.20  43757.70  22076.90  24749.40  38741.40  23464.00
## 39   78528.70  55595.80  25202.90  64020.50  36493.00  53736.90  34379.00
## 40   17306.20  47928.30  54462.60  28607.90  35615.50  40731.30  24289.60
## 41   87190.10  82372.00  65316.30  97743.10  87542.10  71645.40  74336.10
## 42   70991.50  64696.30 116643.00  46351.00  77578.20  60725.60  79202.70
## 43   51855.40  53305.30  40436.10  49091.90  54131.80  63263.30  59618.70
## 44   53537.60  55073.10  62020.90  58509.00  65030.40  56484.00  82179.10
## 45   65207.90  52736.80  40575.30  80284.60  79485.10  75326.80  92066.60
## 46   83962.30  50995.00  59392.20 167746.00  69528.30  65790.40  74413.90
## 47   87910.70  70047.30  78441.20  69992.30  83822.70  97645.20  72033.50
## 48   63707.30  53471.10  70522.50  76879.40  85399.80  75939.20  67428.70
## 49   58797.50  36027.70  38894.80  44512.20  54203.40  42589.60  38393.20
## 50   33436.80  28216.70  53468.30 136615.00  48110.60  48271.90  54163.80
## 51   40099.20  42087.70  20003.60  17964.20  45681.40  46372.50  46647.20
## 52   30087.90  48232.30  61038.20  57704.40  44058.60  53424.30  46798.60
## 53  169385.00  45771.00  95850.70  97079.90 206409.00  73687.50  78370.00
## 54   57561.60  42139.70  98915.90  57097.50 136840.00  67918.80  44452.70
## 55   31931.80  36930.90  32720.50  41999.70  50772.00  53186.10  25559.00
## 56   26798.00  22005.30  21278.20  26687.00  26624.20  50972.30  73492.30
## 57   67464.60  57985.00  30169.10  33196.30  56446.20  26775.20  11295.60
## 58   13515.50  24829.50  14849.00   8958.79  17577.70  17985.70  11667.60
## 59   20988.10  35814.10  21815.50  55271.10  40820.10  28911.80  19807.50
## 60   25620.00  26022.40  23062.50  19213.10  23431.90  24747.90  13295.70
## 61   74548.70  14374.40  27729.20  35251.10  28664.80  56005.60  62197.70
## 62   56926.40  91032.70  22399.70  22524.10  48220.40  47287.70  25346.10
## 63   26606.40  37245.70  64418.30  23317.50  53185.10  46552.20  36565.30
## 64   27241.80  19267.30  41349.80  39994.10  39024.30  21827.80  41126.60
## 65   37577.20  54029.70  51799.90  52026.80  43472.20  27403.10  19826.40
## 66   36434.80  26460.00  27343.10  25893.40  27493.70  40020.90  35205.30
## 67   21625.60  35036.30  53054.10  42906.60  29998.50  30330.40  27412.60
## 68   29084.60  32612.90  26490.90  35776.10  26076.90  27013.70  34750.90
## 69   29704.30  29007.10  29859.50  38185.30  40237.80  37542.10  43658.50
## 70   47875.40  40525.90  34952.60  32106.10  34650.90  38740.80  44034.20
## 71   42752.10  31049.00  29008.80  47050.00  37673.10  33457.50  35055.10
## 72   36631.40  28237.60  37168.50  42181.30  16278.90  32617.00  57668.90
## 73   21600.10  27927.60  19531.10  23451.00  12210.90  31418.50  41272.90
## 74   30232.80  41330.50  48160.60  35178.50  29683.70  25724.80  28460.50
## 75   24988.80  48507.30   2002.53  35501.80  24242.10  22694.20  28882.90
## 76   46208.90  46630.20  46633.40  44677.40  34404.20  22995.80  32095.20
## 77   36985.80  24290.70  34244.80  25997.50  24932.80  37709.20  52554.10
## 78   44567.40  28315.40  23346.40  34831.30  27192.70  16209.40  59373.60
## 79   18769.80  63289.10  24362.30  29537.20  48168.40  40856.50  28373.50
## 80   26108.60  34241.60  40259.40  82479.90  64308.20  29872.60  47146.80
## 81   67720.30  99220.40  90936.80  69463.80  71889.40  50879.00  84534.20
## 82  118946.00  48276.10  80511.80  60484.10  79405.80  86230.00  62810.00
## 83   38855.90  49443.30  55330.10  62415.50  58148.40  58090.60  55386.40
## 84   65250.40  61015.00  64998.50  58129.90  73731.70  58268.80  83999.60
## 85   38115.80  78270.70  89202.20  74317.70  97839.10  76591.40  48257.30
## 86   61542.40 176727.00  68658.30  56195.20  75607.50  54179.40  92637.10
## 87   81895.20  72797.90  86179.90  89626.70  75548.00 121743.00  89242.50
## 88   66692.30  79967.20  92698.90  83509.40  69880.70  56074.30  69662.20
## 89   39235.10  44616.20  54850.70  44272.40  38608.70  41736.50  41878.50
## 90   52274.40 138830.00  47909.30  47039.50  55701.60  21740.40  41206.70
## 91   22536.00  18661.80  44132.20  41766.80  46289.20  38153.20  65853.80
## 92   61806.00  65214.00  42803.70  57942.50  48566.50  32956.10  31806.50
## 93  105152.00  95394.30 215572.00  82666.10  84965.80  61125.40  64830.40
## 94   96691.10  57607.90 132799.00  67845.70  43408.50  90838.70  45585.00
## 95   33360.30  42918.90  50346.90  56710.50  25711.60  44438.80  41542.70
## 96   20769.60  28212.10  27106.80  51189.90  76129.40  87554.50  49980.30
## 97   39741.30  34052.20  55917.40  26878.60  11470.70  39530.70  14456.70
## 98   15407.70   9118.39  18351.70  18756.90  11908.80   9048.19  12711.00
## 99   23008.50  57348.20  41054.30  31943.70  20473.50  39541.70  22755.80
## 100  23367.60  20166.60  23635.90  25053.90  13666.50  20668.90  24218.50
## 101  29616.50  33123.30  28876.80  57179.20  61687.70  93053.70  34808.60
## 102  20927.20  26608.50  50055.20  48630.50  24749.70  25021.20  43442.20
## 103  68949.20  23696.80  55454.20  55728.60  36433.50  50241.80  28297.10
## 104  42045.60  39668.30  41255.50  22211.20  37344.40  35488.70  28268.80
## 105  52024.50  52469.70  46129.50  27613.30  19903.70  29426.80  31686.30
## 106  27131.10  27399.40  30085.30  39372.60  35550.10  36616.00  39852.90
## 107  55053.80  40925.50  30818.30  29869.30  29090.70  28628.80  26673.90
## 108  27297.00  36375.40  32607.90  27856.20  36197.80  36246.00  31402.00
## 109  30925.40  41446.70  45204.10  43770.50  48119.80  54570.90  51309.00
## 110  37411.40  32274.20  35011.90  40164.60  44923.60  49920.40  39387.00
## 111  28730.00  50508.20  38338.50  32898.70  37706.20  52588.40  43415.10
## 112  37794.20  32610.00  17806.30  33210.10  56762.80  48231.90  23581.40
## 113  17581.10  23181.00  11152.50  32475.20  43712.50  24209.00  26400.80
## 114  49419.50  34914.30  29025.00  26517.30  29771.30  21323.30  20399.00
## 115   2027.83  30459.60  25937.00  24104.50  30690.10  20503.50  23319.00
## 116  50161.70  42623.70  33560.00  22151.30  29839.90  32382.90  42189.50
## 117  38907.10  17917.10  25761.90  39950.30 105603.00  19966.30  29792.40
## 118  23688.30  35257.50  27336.90  18258.40  75947.20  63407.80  26288.20
## 119  25266.50  29904.90  48701.90  42188.90  29269.90  20173.30  21882.40
## 120  36706.10  85214.00  64537.40  31282.40  47600.30                    
##           Aug       Sep       Oct       Nov       Dec
## 1    93734.20  60046.80  31928.00  59888.00  40904.30
## 2    49787.40  54561.00  70270.60 117975.00  36106.00
## 3    31941.00  34415.10  43688.10  22532.80  41869.60
## 4    76397.50  32432.70  33352.90  28201.20  44039.60
## 5    30049.60  31726.60  30964.00  27202.70  14651.20
## 6    13353.50  83792.40  86123.80  54893.70  19110.10
## 7    64047.60  79293.70  95449.80  40411.70  74609.40
## 8    49667.70  35606.40  27489.20  71965.10  43264.10
## 9    35825.90  36989.10  49701.90  62771.50  47626.70
## 10   77953.60  28512.30  42574.40 160879.00  50598.40
## 11   21819.00  42512.90  39549.80  34424.10  23808.90
## 12   60868.50  24002.40  39348.10  31071.10  36073.80
## 13   53007.40  30507.00  71670.90  59479.70  24959.70
## 14  101924.00  83879.90  67800.90  65491.80  66318.80
## 15   36941.60  57238.80  73357.90  46606.40  42919.00
## 16   23377.10  12143.20  13076.20  31991.80  16775.70
## 17   86680.20  39539.70  91408.70  50802.10  30667.20
## 18   37508.40  42185.00  20727.80  17713.00  16651.80
## 19   44203.30  71697.00  74405.10  28182.70  34634.70
## 20   21352.40  28954.60  51127.00  20565.10  21803.70
## 21   35874.50  61987.90  64805.40  37620.50  55895.30
## 22   82116.00  87907.80  61853.50  85990.20  25030.40
## 23   33355.50  41366.70  36106.40  37322.50  27974.50
## 24   29327.90  88750.30  16129.30  35556.50  12004.30
## 25   70491.00  36528.70  30148.00  36019.60  19747.40
## 26   38835.20  18993.50  28778.30  47469.00  24051.10
## 27  143760.00  92251.60  26439.10  59632.10  45283.00
## 28   54065.10  71993.60  49168.80  64163.60  48320.20
## 29   48355.40  18527.30  35316.60  29809.20  38025.40
## 30   27557.30  28459.70  21950.60  37064.40  66559.70
## 31   52289.40  51470.10  54906.20  79085.00  46678.50
## 32   62278.80  60472.90  42893.40  32251.80  61466.00
## 33   50281.70  57578.20  33889.30  55148.90  21437.80
## 34   55857.80  28964.20  42704.40  29812.10  26558.10
## 35   84482.30  39987.30  26987.30  18235.70  32093.90
## 36   84351.80  41625.20  57276.70  45464.80  37431.80
## 37   25654.90  23057.50  54200.70  39800.70  27393.40
## 38   39772.60  42783.20  57049.60  52648.00  22117.00
## 39   25055.60  72433.30  11799.50 104688.00  22352.50
## 40   28050.20  46371.60  88959.20 139449.00  78877.00
## 41   50999.70  83197.70  77236.30  71461.10  57161.20
## 42   89019.20  62049.30  63468.50  62573.60  81803.20
## 43   56102.80  53894.70  83352.50  38396.80  53295.10
## 44   59798.60  79446.30 119806.00  58866.50  91589.20
## 45   75717.20  49367.00  82365.40  78330.90  62805.10
## 46   49647.00  93336.80  95417.00  86324.00  72590.30
## 47  104300.00  87753.60  42155.50  52775.80  38001.90
## 48   60460.00  64071.40 100991.00  62686.50  72966.50
## 49   41898.70  42388.50  33288.60  41684.60  42898.70
## 50   21478.60  41432.90  44805.60  40482.70  32834.30
## 51   33398.40  64846.90  38249.40  23948.40  29812.10
## 52   34614.80  28909.90  39538.40  44002.60  80154.30
## 53   59327.70  64958.40  68225.00  31915.50  56719.50
## 54   92084.00  45668.50  42972.50  47494.30  45394.40
## 55   41504.50  40184.80  24789.50  29532.40  27057.90
## 56   86242.90  49834.80  65591.80  68112.50  41711.30
## 57   39811.60  15143.20  34195.40  29704.60  17406.80
## 58    9200.38  11976.30  36852.80  38324.10  15915.40
## 59   38590.20  25386.40  10924.80  15902.10  49710.60
## 60   19943.10  25580.50  11043.10  18433.10  47869.90
## 61   85487.70  35093.10  62421.90  71307.30  18917.90
## 62   23830.00  48587.70  32125.10  30509.60  33075.20
## 63   50303.80  28421.10  43817.00  25555.10  19188.20
## 64   35628.40  28496.00  29596.80  34233.00  33457.70
## 65   27625.10  31413.30  36019.90  26603.50  28814.50
## 66   35504.50  42005.00  16482.30  18081.10  38446.30
## 67   28149.40  26910.40  35947.70  23373.30  38402.20
## 68   34098.50  29335.30  21720.00  47937.20  70492.00
## 69   43574.10  47157.00  31181.70  56457.40  40229.30
## 70   47189.50  37374.30  34373.00  34486.60  41394.30
## 71   48215.80  43445.00  61372.40  42202.20  48395.50
## 72   44456.70  23644.50  46231.70  33445.80  26381.30
## 73   24083.90  34922.00  28168.90  30143.00  38542.60
## 74   21328.40  21713.10  18924.60  18729.20  32323.20
## 75   21469.30  25358.30  23481.90  25838.20  84919.80
## 76   35636.00  42136.10  30612.80  33156.00  24687.40
## 77   18201.20  28800.80  49886.90 115885.00  78951.50
## 78   56608.20  25512.30  67635.90  57562.90  17940.50
## 79   19094.40  21248.80  24228.40  18107.90  43924.70
## 80   88936.80 145273.00  73620.70  88034.70  82490.50
## 81   77419.40  68923.00  60085.70  71770.90  64575.00
## 82   61739.00  54679.80  82712.90  52754.80  59448.10
## 83   84512.90  40204.00  55241.20  54526.60  56204.90
## 84  123048.00  60200.20 105987.00  69577.50  50193.10
## 85   82429.00  78522.60  66090.70  82730.70  51851.70
## 86   91187.80  90102.60  78198.40  90141.40  64047.80
## 87   42578.50  51655.60  40775.90  65290.60  50779.50
## 88  100238.00  63901.00  72110.60  55461.00  39702.50
## 89   35904.40  44000.90  43635.10  34340.50  27663.50
## 90   45025.60  40143.00  33348.90  41599.00  41984.20
## 91   37927.70  25551.50  32701.30  28396.10  56801.10
## 92   37848.10  43949.40  78947.50 172133.00  47651.70
## 93   72824.20  34504.20  56704.10  55170.00  43559.20
## 94   45592.90  49161.00  45811.30  31955.90  36781.80
## 95   25865.40  29865.60  27928.50  28233.60  22992.50
## 96   67737.20  66478.60  42327.70  69474.80  59275.10
## 97   33804.10  29665.10  17892.40  13352.30  26721.20
## 98   38084.70  38779.20  16237.10  21399.00  36532.40
## 99    6806.93  16004.20  52447.80  26469.80  25201.90
## 100  10325.00  18637.00  48013.70  76344.20  14801.50
## 101  65171.90  73683.60  19700.20  59894.20  99005.50
## 102  34201.90  31346.20  33833.60  24979.80  37983.10
## 103  45113.30  24610.80  19011.10  28739.20  19865.50
## 104  32324.50  33054.00  34632.60  38992.00  54196.50
## 105  37958.30  26444.50  29688.00  37089.70  27691.00
## 106  17722.30  18198.40  37053.60  24325.50  36648.30
## 107  37519.20  23001.10  38852.30  27867.80  30275.60
## 108  21986.20  50171.70  68392.10  27365.70  32856.00
## 109  33887.60  57466.30  40355.80  48837.80  40360.40
## 110  38216.90  34921.10  42477.10  44480.90  32207.70
## 111  65206.20  43555.40  51550.80  37423.50  29995.00
## 112  49261.90  33340.20  26835.10  21569.50  29069.80
## 113  23393.60  31757.40  42133.10  32607.00  39458.50
## 114  18456.60  19236.60  30849.50  24745.30  40344.40
## 115  26217.20  27173.80  86894.60  47099.90  47106.20
## 116  33881.80  34123.50  26161.20  38154.20  29648.00
## 117  48582.70 120657.00  79194.30  47034.20  62709.30
## 118  67113.60  54090.90  16442.50  18962.40  67934.90
## 119  22885.60  17892.30  44343.60  25443.80  31635.40
## 120
ddata5 <- decompose(tsdata5, "multiplicative")
plot(ddata5)

plot(ddata5$seasonal)

plot(ddata5$trend)

plot(wrace$avg_wage,type ="l",main = "Average salary for all whites",xlab= "Year",ylab="Average Salary")

class(tsdata5)
## [1] "ts"
mymodel5 <- auto.arima(tsdata5)
mymodel5
## Series: tsdata5 
## ARIMA(2,1,3)(2,0,2)[12] 
## 
## Coefficients:
## Warning in sqrt(diag(x$var.coef)): NaNs produced
##          ar1     ar2      ma1     ma2     ma3     sar1     sar2    sma1
##       0.4654  0.3083  -1.2364  0.0203  0.2287  -0.6974  -0.3526  0.7364
## s.e.     NaN     NaN      NaN     NaN     NaN   0.8966   1.0787  0.8606
##         sma2
##       0.4296
## s.e.  1.0595
## 
## sigma^2 estimated as 485528596:  log likelihood=-16348.89
## AIC=32717.78   AICc=32717.93   BIC=32770.44
auto.arima(tsdata5,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,1,2)(1,0,1)[12] with drift         : 32705.05
##  ARIMA(0,1,0)            with drift         : 33370.61
##  ARIMA(1,1,0)(1,0,0)[12] with drift         : 32995.47
##  ARIMA(0,1,1)(0,0,1)[12] with drift         : 32726.98
##  ARIMA(0,1,0)                               : 33368.61
##  ARIMA(2,1,2)(0,0,1)[12] with drift         : 32719.19
##  ARIMA(2,1,2)(1,0,0)[12] with drift         : 32705.16
##  ARIMA(2,1,2)(2,0,1)[12] with drift         : 32663.92
##  ARIMA(2,1,2)(2,0,0)[12] with drift         : 32674.99
##  ARIMA(2,1,2)(2,0,2)[12] with drift         : 32650.25
##  ARIMA(2,1,2)(1,0,2)[12] with drift         : 32706.37
##  ARIMA(1,1,2)(2,0,2)[12] with drift         : 32692.56
##  ARIMA(2,1,1)(2,0,2)[12] with drift         : 32669.88
##  ARIMA(3,1,2)(2,0,2)[12] with drift         : 32678.32
##  ARIMA(2,1,3)(2,0,2)[12] with drift         : 32652.27
##  ARIMA(1,1,1)(2,0,2)[12] with drift         : 32693.18
##  ARIMA(1,1,3)(2,0,2)[12] with drift         : 32689.74
##  ARIMA(3,1,1)(2,0,2)[12] with drift         : 32662.96
##  ARIMA(3,1,3)(2,0,2)[12] with drift         : 32656.5
##  ARIMA(2,1,2)(2,0,2)[12]                    : 32648.25
##  ARIMA(2,1,2)(1,0,2)[12]                    : 32704.41
##  ARIMA(2,1,2)(2,0,1)[12]                    : 32661.93
##  ARIMA(2,1,2)(1,0,1)[12]                    : 32703.09
##  ARIMA(1,1,2)(2,0,2)[12]                    : 32690.6
##  ARIMA(2,1,1)(2,0,2)[12]                    : 32667.88
##  ARIMA(3,1,2)(2,0,2)[12]                    : 32676.36
##  ARIMA(2,1,3)(2,0,2)[12]                    : 32650.44
##  ARIMA(1,1,1)(2,0,2)[12]                    : 32691.25
##  ARIMA(1,1,3)(2,0,2)[12]                    : 32687.78
##  ARIMA(3,1,1)(2,0,2)[12]                    : 32661.03
##  ARIMA(3,1,3)(2,0,2)[12]                    : 32654.51
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(2,1,2)(2,0,2)[12]                    : Inf
##  ARIMA(2,1,2)(2,0,2)[12] with drift         : Inf
##  ARIMA(2,1,3)(2,0,2)[12]                    : 32717.78
## 
##  Best model: ARIMA(2,1,3)(2,0,2)[12]
## Series: tsdata5 
## ARIMA(2,1,3)(2,0,2)[12] 
## 
## Coefficients:
## Warning in sqrt(diag(x$var.coef)): NaNs produced
##          ar1     ar2      ma1     ma2     ma3     sar1     sar2    sma1
##       0.4654  0.3083  -1.2364  0.0203  0.2287  -0.6974  -0.3526  0.7364
## s.e.     NaN     NaN      NaN     NaN     NaN   0.8966   1.0787  0.8606
##         sma2
##       0.4296
## s.e.  1.0595
## 
## sigma^2 estimated as 485528596:  log likelihood=-16348.89
## AIC=32717.78   AICc=32717.93   BIC=32770.44
library(tseries)
adf.test(mymodel5$residuals)
## Warning in adf.test(mymodel5$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel5$residuals
## Dickey-Fuller = -10.855, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel5$residuals)

acf(ts(mymodel5$residuals),main = "ACF Residual")

pacf(ts(mymodel5$residuals),main = "PACF Residual")

myforecast5 <- forecast(mymodel5, level =c (95), h= 3*12)
plot(myforecast5,main = "Salary forecasting for all whites in next 3years",xlab = "Time", ylab ="Average salary")

myforecast5[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 120                                              45781.88 42961.61
## 121 41862.71 42282.04 42702.85 41518.70 39489.24 38157.52 40283.93
## 122 40113.54 42470.95 40530.14 39275.68 42229.34 41467.26 40565.86
## 123 39725.54 37819.71 38929.63 40142.53 38731.98                  
##          Aug      Sep      Oct      Nov      Dec
## 120 43212.90 38161.66 39385.30 40030.80 41188.51
## 121 38293.63 39771.72 41494.42 40452.27 38472.56
## 122 41531.75 41999.46 40132.35 40435.36 41244.23
## 123

forecasting for race 2: Black or African American

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
brace <- subset(newrace, race_name == "Black or African American",select = c(avg_wage, year,race_name))
head(brace)
brace$avg_wage <- as.numeric(brace$avg_wage)
brace$Date <- paste (brace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata6 <- ts(brace$avg_wage,frequency = 12)
tsdata6
##           Jan       Feb       Mar       Apr       May       Jun       Jul
## 1    48178.60  29250.20  50330.30  53659.00  47527.00  32677.90  70695.00
## 2    45883.90  97502.70  32346.40  42188.70  26625.00   5689.51  15140.30
## 3    73606.30  93783.50  23891.80  28856.00  28124.70  31060.40  25406.30
## 4    33334.30  29707.00  46985.70  31204.60  51243.70  31534.90  32892.90
## 5    43800.50  25926.20  28036.20  25401.50  27817.20  19199.20  12937.70
## 6    55165.20  96550.00  17694.00  33228.70  68800.60  43300.70  20375.10
## 7    53026.40  42219.40  52504.20 180532.00  79147.70  37210.90  82190.30
## 8    38313.70  45320.90  35091.70  23558.90  55211.30  35220.40  25587.80
## 9     8736.67  34381.40  29952.30  43100.80  70533.20  38084.60  51838.60
## 10   27451.60  54174.60  25619.00  46391.70 114335.00  71564.10  34108.30
## 11   30252.50  19338.20  33032.20  30493.80  34236.10  22675.70  30635.80
## 12  123330.00  39143.30  28782.70  36712.90  56895.10  22692.50  41209.00
## 13   50559.80  24895.90  56102.00  52710.70  32167.60  42030.50  36809.70
## 14   55608.10  59788.30  59113.10  67329.70  42511.80  58046.20  40995.30
## 15   50711.80  47684.30  40054.60  29858.70  58405.10  45971.00  43662.50
## 16   33332.90  14265.40  30757.00  16251.60  36720.70   8911.58  47665.10
## 17   69852.50  27439.60  19379.80  19896.30  19400.80  19683.40  18693.20
## 18   27637.00  17050.10  28710.60  36261.40  12697.90  19983.50  24434.00
## 19   31553.90  36615.50  57878.40  27553.90  23247.10  30434.40  46366.50
## 20   21327.60  18217.80  86012.10  40677.70  15849.20  19920.70  37774.70
## 21   38184.00  43938.50  16409.90  30558.70  41882.20  17353.50  22923.70
## 22   66493.80  27449.40  71682.60  76462.60  57964.50  26502.00  31007.10
## 23   26645.80  27839.10  73144.60  39799.40  14239.50  33252.10  19446.30
## 24   63709.80  18033.00  38130.50  32093.20  27530.00  40547.90  38591.50
## 25   32821.30  20305.00  32112.40  32400.50  44985.80   1354.15  41181.00
## 26   24523.00  12158.90  14802.00  36490.40  56419.50  55688.40  39557.80
## 27   54582.70  65547.60  27321.50  22492.50  56560.80  35313.70  25724.70
## 28   55210.20  28235.20  29060.80  44306.50  27835.60  17799.80  29266.50
## 29   24028.70  36150.80  12708.40  19944.00  53893.10  25329.60 109414.00
## 30   35764.10  55792.60  50378.60 181223.00  36935.20  24603.50  54664.90
## 31   78321.60  33705.60  24501.50  20894.80  16525.30  10240.40  32072.00
## 32   44600.30  39890.40  51990.30  38928.70  47134.70  12243.90  45907.20
## 33   22462.20  22445.60  22939.10  24920.20  57078.20  30147.00  29473.50
## 34   26709.40  30789.10  77124.80  37789.20  47029.40  51553.70  43963.20
## 35   35541.80  38060.00  26807.90  16412.00  32852.90  43062.30  26821.30
## 36   17223.80  21873.50  29797.30  27422.50  20611.60  44038.00  29333.70
## 37   22854.50  22316.40  32739.90  36550.40  16474.50  26133.90  26698.80
## 38   46476.90  24783.90  38549.80  29186.90  45826.80  33060.90  21827.00
## 39   42124.20  43070.40  30261.40  32499.80  24946.70  31008.70  25673.90
## 40   42303.30  53455.40  76071.60  56782.40  55594.90  75390.70  46117.20
## 41   55928.40  58157.10  36517.50  27087.10  35647.00  74811.00  56327.90
## 42   24246.70  45245.30  42315.60  51835.50  49173.70  46020.20  43953.90
## 43   38635.10  41774.00  47484.00  62940.50  60104.60  70912.50  55377.90
## 44   20712.10  45403.50  61066.10  59080.40  67396.50  39802.20  71624.80
## 45   52111.90  26030.10  73783.10  27789.70  64953.20  78166.60  75435.40
## 46   55780.70  76096.50  62842.10  31598.80  50645.60  26378.00  18916.80
## 47   57971.40  52571.10  49732.40  59643.70  72224.00  36004.60  39043.40
## 48   38779.20  37643.30  27751.20  26217.90  41765.40  31170.90  44998.60
## 49   21512.10  40433.40  43780.70  40064.60  29208.20  32280.60  36700.10
## 50   49210.30  36252.70  70139.50  12255.10  40358.40  27993.50  24236.70
## 51   21008.80  26157.40  24338.60  35213.40  32282.80 117159.00  30787.00
## 52   48064.80  54390.40  57847.90  17333.60  65322.50  50739.90  43182.10
## 53   38471.20  43039.70  47882.10  44937.20  30718.30  36106.90  32592.30
## 54   39716.10  26782.60  24255.00  27051.30  26651.50  35422.80  30444.70
## 55   40061.30  56245.80  58848.10  41369.10  72552.20  59507.00  27561.70
## 56   19361.80  24973.80  20932.40  17276.20  14538.40  24716.10  12615.90
## 57   16750.20  33170.20  31452.50  17567.70  19656.50  25882.00  19360.00
## 58    7338.75  17048.80  30961.50  11584.60  19719.70  19134.50  40267.60
## 59   15957.70  15622.20  37168.50  49566.50  12975.60  17861.40  22383.30
## 60   37035.70  44195.60  10447.80  32279.20  77213.50  17346.10  24193.70
## 61   33495.80  37095.00  32156.90  14301.50  35371.60  47648.40  25624.70
## 62   32025.60  26495.30  21689.40  27693.40  14495.30  45694.90  41967.70
## 63   22088.60  36983.40  31233.20  28043.20  49196.80  45698.90  44060.20
## 64   35012.00  30376.30  24813.80  28788.90  20653.30  27322.30  19560.30
## 65   16836.30  22462.70  43281.50   7631.27  40221.30  39077.20  51283.40
## 66   36868.20  26438.30  36542.20  20978.20  29716.30  27194.40  34332.80
## 67   33402.50  40248.60  63903.70  23161.80  23522.30  28031.10  42571.30
## 68   35674.60  42873.00  16690.20  33565.30  39049.30  24422.20  37368.30
## 69   40117.70  42656.70  28970.70  19341.90  39599.30  34317.90  40902.40
## 70   38069.50  30275.80  33110.50  15262.80  47885.70  27710.80  32577.70
## 71   22399.30  21964.20  23816.00  31168.00  24703.90  29949.90  29298.80
## 72   23111.30  33229.10  29100.60  24447.40  29439.60  10252.20  18284.90
## 73   10299.50  26551.70  13065.40  41171.20  30855.30  27583.00  87181.30
## 74   21577.20  28298.20  24255.50  27237.00  19647.30  25090.50  27930.40
## 75   31262.90  26827.30  14766.10  30721.10  45138.10  98093.90  64669.10
## 76   14849.10  63547.00  57637.60  33946.10  30643.80  37529.90  21067.30
## 77   26544.80  31612.80  19411.20  21762.00  14780.40  16832.80  28451.30
## 78   29525.00  40027.50  60078.40 100552.00  46153.10  62918.20  45233.70
## 79   46898.30  49897.90  60258.70  71032.20  47595.70  52895.90  53795.60
## 80   42938.40  55623.90  44369.40  40556.60  62527.10  38218.90  78994.90
## 81   47882.00  43429.30  54644.90  27144.10  48698.30  39618.80  43952.70
## 82   69565.60  52476.20  51790.20  43700.80  48606.40  51817.90  50828.30
## 83   35175.30  73146.00  47168.90  60104.30  92152.40  43921.50  48484.30
## 84   72085.70  80301.60  73380.80  69010.60  94880.60  54289.50  60594.50
## 85   53713.90  25542.90  19499.70  85866.80  47049.50  90137.90  95077.80
## 86   66293.70  34654.70  38448.50  39716.10  57214.40  37957.60  35565.80
## 87   36133.30  48649.40  81248.20  39470.90  38456.20  47151.00  21201.70
## 88   39957.90  24640.90  23716.70  30226.90  13113.40  32289.30  26359.20
## 89   28997.60  25439.60  47081.70  32556.00  40916.20  51389.40  20195.60
## 90   31412.90 120911.00  94890.20 167285.00  52268.80  49793.00  52680.10
## 91   72591.80  57915.60 151276.00  71590.90  25347.90  89646.40  39253.90
## 92   34104.00  34365.70  35358.00  41501.60  23300.70  39670.50  45183.90
## 93   31299.30  29138.90  23963.90  60830.10  68992.90  74923.80  43207.80
## 94   45294.30  30142.70  39900.20  25317.10  10255.40  39931.10  19875.20
## 95   12623.90  10617.40  15899.70  16144.30  13140.00  12884.20  17349.60
## 96   21613.70  31485.30  28669.00  23565.00  33243.10  12342.40   7209.14
## 97   38438.70  22485.10  15277.20  18300.30  19963.00  21273.80  15909.80
## 98   24701.20  18604.60  41897.80  43064.10  40589.90  33991.20  40831.00
## 99   28258.20  30050.30  41438.70  26068.60  20781.80  42565.00  33132.80
## 100  25705.80  35909.10  34819.60  34011.10  29442.10  23975.60  31020.50
## 101  45623.60  26509.00  21625.70  30064.00  30309.20  23816.20  22691.70
## 102  44137.80  40213.60  25002.80  19546.20  28885.50  30543.30  28736.10
## 103  20679.60  29293.50  24162.10  30655.10  34862.20  24091.60  17850.20
## 104  51117.10  28678.60  17067.90  22858.80  23608.10  24496.30  37790.10
## 105  35585.50  24738.90  26357.00  31204.20  36218.90  29312.20  15567.30
## 106  41212.60  33150.30  39400.80  86899.10  17309.40  52390.80  35575.50
## 107  39970.00  61936.40  38881.90  35828.50  28951.00  28785.60  38830.60
## 108  34400.50  41170.90  13296.50  43570.80  44728.90  38859.20  33370.90
## 109  33020.30  39530.70  29241.00  20432.20  21576.00  23495.80  21491.40
## 110  33570.10 401780.00  30048.90  43507.60  11280.20  33655.10  28893.20
## 111  29664.30  17519.90  15852.10  35984.10  18136.60   9611.06  24260.70
## 112  36903.70  44210.30  32636.20  42957.30  23672.10  20617.90  24825.20
## 113  35214.70  21913.60  44510.90  36561.00   3169.26  31214.00  19365.90
## 114  25400.90  27880.50  35538.60  24291.10  18292.40  67909.60  60690.10
## 115  49617.90  21448.60  38793.10  45365.30  28029.10  32712.40  19603.60
## 116  20059.70  26183.70  74940.60  69915.10  32174.10  43269.80          
##           Aug       Sep       Oct       Nov       Dec
## 1    35619.80  39364.50  19365.30  21903.90  55689.00
## 2    58975.50  56137.90  56568.70  33743.10  27099.00
## 3    17673.20  41439.90  22133.80  19753.00  23667.20
## 4    34497.90  32813.80  63754.90  38756.20  28572.50
## 5    25315.30  24652.10  93748.90  41573.50  46312.00
## 6    39038.70  39021.70  33799.60  13430.20  64758.20
## 7    88718.00  34209.10  67288.00  41730.30  59585.70
## 8    17727.40  26327.00  41609.20  34606.50  22088.10
## 9     8590.17  26071.10  24314.00  21704.30  17518.30
## 10   70032.40  43378.10  29488.40  35350.10  12644.00
## 11   49356.00  33419.40  58053.30  36657.50  85162.70
## 12   56603.60  32595.00  10704.50  43765.40  46238.00
## 13   42865.30  22817.10  25874.50  56939.40  46365.60
## 14  104821.00  21967.10  27536.60  33451.60  24464.00
## 15   38099.00  57346.10  37370.10  51071.50  26310.60
## 16   30631.20  11561.40  14390.10  24148.20  29551.10
## 17   30035.10  22484.70  36848.00  40284.50  35544.50
## 18   58806.60  46422.60  38047.10  68427.70  66713.00
## 19   51393.60  27525.30  19382.90  24147.20  53593.10
## 20   18677.80  41605.30  29264.50  66225.00  35609.20
## 21   31053.60  52430.50  49846.10 126505.00  37652.40
## 22   47865.60  35867.50  33987.30  28198.90  21330.00
## 23   24899.10  26309.90  14876.70  49235.90  17984.90
## 24   60614.20  39343.20  41687.00  22926.20  34183.50
## 25   21644.30  27851.20  26002.20   9371.39  12458.20
## 26   30613.10  18347.30 148458.00  83642.70  23749.80
## 27   48055.20  31543.60  34856.80  48633.30  45239.30
## 28   25888.30  25115.60  40803.80  19192.70  20759.30
## 29   99467.30  42548.90  29018.60  21617.30  19433.70
## 30   59317.70  49451.90  49373.00  44730.10  45792.70
## 31   42809.20  38224.40  46326.60  41538.80  38927.80
## 32   70374.30  56130.90  40463.70  60100.20  27305.70
## 33   15386.60  65781.80  19181.20  41546.30 396311.00
## 34   30673.00  39270.10  30912.50  25461.60  29165.70
## 35   39562.40  73941.90  42055.40  50042.50  45148.80
## 36   29301.90  25461.30  20243.10  53187.10  18723.30
## 37   26325.20  35757.10  26796.50  13522.40  67347.80
## 38   68374.80   5931.72  80115.80  30399.10  15936.50
## 39   59260.80  64453.70  96560.60  45930.40  58742.00
## 40   42591.30  61154.30  66839.20  40336.30  53058.70
## 41   46420.90  41505.80  40050.60  62371.90  36239.20
## 42   51375.30  22401.90  48099.10  40700.50  43302.30
## 43   56122.90  50479.20  44250.00  48958.50  55997.90
## 44   47408.30  56255.80  85293.10  41505.00  49222.30
## 45   82768.70  86915.00  61162.50  64792.00 133678.00
## 46   49944.80  87110.00  47999.90  93891.40  55256.10
## 47   28613.00  57118.60  28168.70  37832.40  34843.50
## 48   53654.70  81991.00  41161.70  38153.30  46309.60
## 49   20862.70  22870.00  32427.60  16796.30  32374.20
## 50   25095.40  57856.20  31308.00  38410.60  56243.70
## 51   25754.30  96484.50 186829.00  46517.10  54727.80
## 52   69256.10  56217.20 149710.00  25031.60  91271.40
## 53   34928.00  39162.60  40545.00  23545.00  39204.70
## 54   27603.70  24010.10  56763.40  60633.70  86161.90
## 55   26655.60  44734.80  24983.60   7240.76  31689.90
## 56    8863.53  15744.00  15912.60  12850.50  13034.60
## 57   23269.50  27626.30  24812.80  28791.50  12167.70
## 58   21409.40  13931.70  17689.20  20035.30  20863.30
## 59   18824.60  35699.70  42315.90  41839.80  35099.60
## 60   29384.60  39930.60  24347.70  26585.20  42033.80
## 61   33890.60  32808.70  31035.90  35440.10  24301.30
## 62   23998.60  22099.60  28890.70  30501.30  24884.80
## 63   35777.00  25466.30  20575.10  31816.20  31085.00
## 64   29176.40  23039.80  30399.10  35329.90  23222.10
## 65   28222.30  18261.70  24494.40  23606.80  25054.50
## 66   25274.90  25302.90  30275.10  33999.10  34609.20
## 67   31288.20  39719.60  85814.70  17581.80  50068.90
## 68   73263.70  43475.50  31566.90  28518.90  30464.30
## 69   40993.30  13306.00  41386.70  42693.90  34968.70
## 70   42291.60  32277.90  29373.80   6207.84  21645.40
## 71   33055.70 396767.00  29844.70  41623.90  21354.60
## 72   17905.40  29482.60  20052.40  35535.00   5365.47
## 73   34380.50  43540.60  33574.40  42154.70  23129.80
## 74   29108.40  23896.30  43955.50  32441.50  11869.70
## 75   37602.40  27604.30  27774.40  34160.80  24373.20
## 76   21377.40  44259.40  21849.80  42108.80  43399.20
## 77   26247.30  14600.10  36703.00  74548.80  67047.80
## 78   49696.90  70866.10  57140.90  60138.00  80271.00
## 79   61885.00  34476.40  26817.50  35841.70  72957.60
## 80   22341.30  46295.10  42855.30  49476.30  61684.80
## 81   35265.00  44388.80  48307.10  62758.70  58280.40
## 82   22842.80  55018.40  59726.90  61894.60  69914.70
## 83   52394.60  56472.50  37080.10  87423.60  28098.10
## 84  130497.00  56048.10  74669.60  80210.90  31139.20
## 85   59040.00  64378.10  43555.30  51176.10  60191.90
## 86   40361.80  37904.20  28596.40  28855.90  40602.30
## 87   40415.00  43551.70  40739.30  27879.70  32527.80
## 88   38876.30  85035.80  30147.80  36151.70  28152.10
## 89   24799.00  31718.60  39862.10  31770.80 132263.00
## 90   81779.10  17186.20  66933.60  48979.00  38106.00
## 91   30614.20  49469.70  39093.00  33647.90  35302.10
## 92   25337.50  26134.40  27252.40  26576.00  35324.60
## 93   56459.20  58238.40  41088.00  70270.90  58228.20
## 94   26847.00  20833.80  18316.30  15790.50  22986.80
## 95   29814.50  34865.50  18364.30  20684.40  30470.20
## 96   14604.20  36419.00  12207.80  19111.70  20954.40
## 97   15507.60  37422.90  48136.20  13340.40  22732.40
## 98   39014.40  11920.60  31489.50  71260.00  17671.10
## 99   38313.10  32672.40  11836.40  36917.90  46879.20
## 100  27263.70  21877.00  30170.40  19141.00  44978.80
## 101  34135.50  29892.30  33680.90  48474.00  48634.10
## 102  30503.50  24514.40  28475.70  19762.40  28578.90
## 103  23593.30  43311.40   7690.80  40810.60  42650.70
## 104  26229.40  36431.20  28936.80  22639.20  28398.80
## 105  29767.40  80665.40  23280.80  26090.60  31855.70
## 106  40893.00  22447.90  40266.20  39542.70  24013.50
## 107  39426.90  29303.20  20292.70  29382.20  37003.80
## 108  32461.70  46539.60  25153.70  44651.70  25557.70
## 109  25126.50  22577.90  25521.20  20859.00  30799.50
## 110  26366.90  29543.80  13101.20  20846.30  19981.80
## 111  12887.50  26361.80  30346.70  13298.50 100817.00
## 112   2760.32  27799.70  21366.80  27177.70  29399.70
## 113  32931.70  46372.20  93926.30  63516.20  37259.10
## 114  22902.50  31827.50  37431.80  27647.80  19838.70
## 115  21716.10   9525.84  16569.70  30916.90  27563.40
## 116
ddata6 <- decompose(tsdata6, "multiplicative")
plot(ddata6)

plot(ddata6$seasonal)

plot(ddata6$trend)

plot(brace$avg_wage,type ="l",main = "Average salary for Blacks or African Americans",xlab= "Year",ylab="Average Salary")

class(tsdata6)
## [1] "ts"
mymodel6 <- auto.arima(tsdata6)
mymodel6
## Series: tsdata6 
## ARIMA(1,0,1) with non-zero mean 
## 
## Coefficients:
##          ar1      ma1       mean
##       0.9006  -0.7813  39310.632
## s.e.  0.0363   0.0542   1520.541
## 
## sigma^2 estimated as 6.69e+08:  log likelihood=-16047.92
## AIC=32103.84   AICc=32103.87   BIC=32124.78
auto.arima(tsdata6,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 32118.77
##  ARIMA(0,0,0)            with non-zero mean : 32200.79
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 32162.03
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 32161.66
##  ARIMA(0,0,0)            with zero mean     : 33790.82
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 32110.21
##  ARIMA(2,0,2)            with non-zero mean : 32109.15
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 32116.9
##  ARIMA(1,0,2)            with non-zero mean : 32106.36
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 32115.12
##  ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 32107.54
##  ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 32116.34
##  ARIMA(0,0,2)            with non-zero mean : 32151.97
##  ARIMA(1,0,1)            with non-zero mean : 32104.56
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 32113.13
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 32105.56
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 32114.39
##  ARIMA(0,0,1)            with non-zero mean : 32166.13
##  ARIMA(1,0,0)            with non-zero mean : 32158.05
##  ARIMA(2,0,1)            with non-zero mean : 32108.27
##  ARIMA(2,0,0)            with non-zero mean : 32138.23
##  ARIMA(1,0,1)            with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)            with non-zero mean : 32103.84
## 
##  Best model: ARIMA(1,0,1)            with non-zero mean
## Series: tsdata6 
## ARIMA(1,0,1) with non-zero mean 
## 
## Coefficients:
##          ar1      ma1       mean
##       0.9006  -0.7813  39310.632
## s.e.  0.0363   0.0542   1520.541
## 
## sigma^2 estimated as 6.69e+08:  log likelihood=-16047.92
## AIC=32103.84   AICc=32103.87   BIC=32124.78
library(tseries)
adf.test(mymodel6$residuals)
## Warning in adf.test(mymodel6$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel6$residuals
## Dickey-Fuller = -11.157, Lag order = 11, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel6$residuals)

acf(ts(mymodel6$residuals),main = "ACF Residual")

pacf(ts(mymodel6$residuals),main = "PACF Residual")

myforecast6 <- forecast(mymodel6, level =c (95), h= 3*12)
plot(myforecast6,main = "Salary forecasting for all Blacks or African Americans in next 3years",xlab = "Time", ylab ="Average salary")

myforecast6[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 116                                                       40385.44
## 117 39884.03 39827.02 39775.68 39729.44 39687.80 39650.30 39616.53
## 118 39473.82 39457.60 39442.99 39429.83 39417.98 39407.30 39397.69
## 119 39357.08 39352.46 39348.30 39344.56 39341.18 39338.15         
##          Aug      Sep      Oct      Nov      Dec
## 116 40278.58 40182.34 40095.67 40017.62 39947.33
## 117 39586.12 39558.73 39534.06 39511.85 39491.84
## 118 39389.04 39381.24 39374.22 39367.90 39362.21
## 119

forecasting for race 3: American Indian

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
Airace <- subset(newrace, race_name == "American Indian",select = c(avg_wage, year,race_name))
head(Airace)
Airace$avg_wage <- as.numeric(Airace$avg_wage)
Airace$Date <- paste (Airace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata7 <- ts(Airace$avg_wage,frequency = 12)
tsdata7
##           Jan        Feb        Mar        Apr        May        Jun
## 1   44442.700  84248.500  55566.800  32824.100   5573.680 108226.000
## 2   28561.500  10289.900  25724.700  32752.200  22916.400  43580.200
## 3   49198.400  12393.900  42353.900  21054.600 202415.000 102796.000
## 4   95832.200  50196.200 116129.000  41577.200  98891.700  30326.700
## 5   10634.200  11092.700  26219.100  21868.100  32261.200  20833.100
## 6   39083.300  40961.500  52013.600  30413.900   8753.090  58089.000
## 7   35355.300  24389.100   7054.160  27484.400  14461.800  52888.200
## 8   15778.100  10182.700  17178.700  40483.000  62286.200  16412.000
## 9   14939.400   3829.480  25830.900  79424.100  85342.600  44695.900
## 10  21437.200  55923.100  21388.500  15638.700 109414.000  37499.500
## 11 157564.000  81445.300  14189.100  42707.800  92108.500  27017.800
## 12  11613.100  25600.900  50421.200  26827.000  11017.800  87530.900
## 13  30416.400    416.662   2048.070   5120.190 184327.000  53057.100
## 14  93001.600  16692.300  37683.100  31073.500   9374.890  44905.200
## 15  33042.900  17291.500  14889.100  34714.900  25109.800  13480.500
## 16  36558.000  34567.000  25600.900  29406.500  33662.700  35520.400
## 17   3276.920  69423.900  38026.300  36386.400  14748.700  39395.500
## 18   9821.450 120355.000  36205.900 104165.000 196615.000  40595.500
## 19  22314.100  28374.400  37499.500   9173.510  10542.500   5042.120
## 20  39079.500  22185.300  22612.100   7291.580  61796.300  82941.000
## 21 127164.000  70913.900  41217.200  57717.000 108377.000  19192.800
## 22   7299.960  14763.100 100126.000  47705.900  41775.000  76891.100
## 23  29036.700  34754.800  48289.200 134137.000  99191.000  39666.800
## 24  55800.900  80101.100  80101.100  42756.900  50479.200  48060.700
## 25  37100.200 111608.000  19827.500  26450.500  39378.600  31068.500
## 26  35335.500  54507.800  11277.400   5126.080   5580.090  45109.500
## 27  48331.400  20857.000  51508.500  26592.700  32019.400  31119.700
## 28  35044.200  32039.400  36793.500  34079.300  11105.400  27258.800
## 29  15019.000  14208.900  10194.400   1312.460  14110.500  10502.200
## 30  39704.500  12115.000  20980.100  30721.300  38631.400  45069.900
## 31  29132.800  10657.200  32306.700  28694.800  60254.100 103868.000
## 32  30522.200   9067.640  33076.200  22942.700  42367.700   6174.180
## 33  38410.900  65082.200  48060.700  42927.100  32532.500  13636.100
## 34  47280.500  30037.900  12506.200  32432.200  30875.500  49255.000
## 35  23220.500  21962.500  61077.100  30231.200  43096.400  37933.000
## 36    417.141  42581.300  37542.700  26357.900  60356.400  25630.400
## 37  37046.800  99125.100  54317.700  44699.400  26249.200  27999.600
## 38  22666.800  12815.800  36847.300  16403.400  88843.500  45885.500
## 39  54228.300  38819.700   2294.270  63766.700  60826.000  69582.500
## 40  64669.500 113687.000  21529.200  83141.000 186871.000  38092.100
## 41  55023.000  43429.200  77862.800 100759.000  19761.300  97154.800
## 42  30227.600  32242.800 100476.000  36804.300 124088.000  31739.500
## 43  71792.300  48668.000  42938.400  22306.200  57422.600  52773.600
## 44  30819.300  25961.200  60495.400  40268.300  38017.100  22469.000
## 45  41526.800  42812.400  54179.100  99664.300  42241.200  21120.600
## 46  16591.300  16159.400  35487.100  11486.500  35038.800  35270.800
## 47  17763.000  11744.900   5027.190  20196.300  14106.200   2053.200
## 48  19006.200  16896.500  12268.100  25413.200  39170.900   5111.710
## 49  10846.700  32714.900  30667.200  64604.300 114199.000 204477.000
## 50  33104.400  23232.700  31604.600  25433.100  40464.800  18136.600
## 51  65904.500  48668.000  46813.600  40351.300  15827.000  22491.500
## 52  14909.200  30301.900  40201.000  42938.400  21422.300  15943.400
## 53  45439.600  30453.300  43490.200  38412.300  63455.700  30833.600
## 54  38017.100  23924.500  31116.700  36717.500  37957.700  15309.700
## 55  37514.900 100378.000  56779.300  45264.200  26580.900  28899.600
## 56  12561.500  34131.100  16610.700  89966.100  46465.300  15335.100
## 57  54913.600  52801.500   2323.270                                 
##           Jul        Aug        Sep        Oct        Nov        Dec
## 1   48233.700  11264.400  16477.800  13230.000  26057.300  45018.200
## 2   21882.700  33465.400  12684.300  13535.900  32269.600  22633.800
## 3    3875.590  55736.800   7082.980  34117.800  47757.300  10433.500
## 4   37889.400  22448.000  15402.100  35012.400  14091.000  29649.700
## 5   44626.800  13129.600   1310.950  37274.900  25141.300   5104.100
## 6   38587.500  31672.900  42353.900  64179.300  67295.900  54166.000
## 7   27623.400  23713.900  39658.900  31224.800  29721.300  71814.700
## 8   20172.500  25799.200  26466.500  12035.500  47047.800  50421.200
## 9   64582.500  45350.200  71118.800 133983.000  31790.200  26259.300
## 10  32603.800  27227.500  88741.400  76802.800  36561.500  31113.100
## 11  31084.000  19088.100  19977.800  42043.600  76589.500  20638.600
## 12  26420.200  48233.700  47821.900  34139.800  38587.000  44648.000
## 13  45057.600  12101.100  98307.600  23022.500  60609.500  16077.900
## 14   2381.410  13601.400  42124.100  24885.000  18619.600  32269.600
## 15  15317.900  98472.200  52922.000  39825.400  36203.800  59083.300
## 16  96467.500  17781.700 176857.000  63951.000  15626.800  25983.200
## 17  48096.400 389134.000  58551.400  28579.300 100191.000  34472.800
## 18 110785.000  39388.900  11296.400  30955.400  36865.300  48233.700
## 19  70832.500  18228.900  25210.600  38841.800  14106.100  53057.100
## 20  57606.000 121260.000  84249.000  64656.800 177061.000  26875.400
## 21  87010.500 184539.000  35699.400 104285.000  56568.100  32306.700
## 22 157745.000  20000.800  95942.400  19078.100  44055.600  48285.800
## 23 127390.000  53118.200  21113.400  35803.800  87110.000  80101.100
## 24  42402.600  22027.800  48289.200  56181.400  52845.200 196841.000
## 25  29090.300  58524.900  38604.600  48289.200  37542.700  26603.400
## 26  96578.500  41008.600  40246.300  38757.600  65537.100  98420.600
## 27  23596.200  19630.200  32294.500  26965.800  25138.700  13439.900
## 28  25099.700  14728.200  17195.700  16352.000  25754.300  10744.800
## 29  29964.400  17736.200  39817.800  17689.000  19720.300  16685.600
## 30   5047.920   3923.530  15223.900   5531.760  22825.800  32374.800
## 31  43429.100 164948.000   2050.430  19541.500  42648.900  32781.100
## 32  22718.600  78034.100  25031.600  25471.900  27559.300  10764.800
## 33  22210.900  27323.000  49061.900  45775.200  30884.100  16096.400
## 34  42402.600  18699.000  15447.700  17117.300  15378.200  38782.400
## 35  61926.000  30448.900  46048.000  11482.400  27079.200  35852.300
## 36  13859.900  13694.300  28369.300  25630.400  15608.000  36907.700
## 37  22027.800  21724.800  42172.600  24886.400  31260.500 190240.000
## 38  16627.600   5109.970  46134.700   8575.030  16277.000  31285.600
## 39  53674.600  65473.900  27147.000 125447.000  71810.000  40491.800
## 40 105603.000  67040.100  32714.900   7392.210  14949.600 101392.000
## 41  17292.500  44612.300  45187.000  29403.600  85621.100  55417.300
## 42  40232.700  88210.700  81113.300  81113.300  81113.300  43297.200
## 43  71891.000  29877.000 117125.000  20492.300  26784.800  40035.400
## 44  35782.000  50379.400  44879.400  11419.900   5190.850  45679.500
## 45  19079.600  32911.300  34424.900  21935.100  35681.400  24143.000
## 46  31963.400  11245.800  27603.200  18106.200  17039.400  18080.800
## 47  14086.300   9816.340  37028.600  17960.300  35567.100  15239.400
## 48   3857.620  16554.100   4637.250  23436.900  29173.800  29330.600
## 49 127594.000   2076.340  26604.800  43187.900  28753.300  28489.600
## 50  25347.900  26872.700  19844.000  22166.900  10945.300  38711.800
## 51  27121.000  49681.900  70974.100  26061.500  60042.800  30417.500
## 52  25189.700  22672.500  15572.500  38518.000  14796.500   5190.850
## 53  11627.500  26110.300  50592.100    422.412  60455.300  43749.700
## 54   9419.620  54409.800  28727.800  25954.200  15341.500  37374.100
## 55  22306.200  25954.200  22166.900  20517.500 192644.000   4258.450
## 56  30227.600   5174.550  45852.500   8161.190  13817.200  16510.000
## 57
ddata7 <- decompose(tsdata7, "multiplicative")
plot(ddata7)

plot(ddata7$seasonal)

plot(ddata7$trend)

plot(Airace$avg_wage,type ="l",main = "Average salary for Americans Indian",xlab= "Year",ylab="Average Salary")

class(tsdata7)
## [1] "ts"
mymodel7 <- auto.arima(tsdata7)
mymodel7
## Series: tsdata7 
## ARIMA(1,0,1) with non-zero mean 
## 
## Coefficients:
##          ar1      ma1       mean
##       0.9369  -0.8483  41260.458
## s.e.  0.0341   0.0532   3178.222
## 
## sigma^2 estimated as 1.215e+09:  log likelihood=-8016.12
## AIC=16040.25   AICc=16040.31   BIC=16058.31
auto.arima(tsdata7,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)            with non-zero mean : 16078.66
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 16071.34
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 16069.7
##  ARIMA(0,0,0)            with zero mean     : 16647.39
##  ARIMA(0,0,1)            with non-zero mean : 16067.7
##  ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 16073.5
##  ARIMA(0,0,1)(1,0,1)[12] with non-zero mean : 16075.45
##  ARIMA(1,0,1)            with non-zero mean : 16041
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 16043.98
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 16041.57
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 16045.93
##  ARIMA(1,0,0)            with non-zero mean : 16065.81
##  ARIMA(2,0,1)            with non-zero mean : 16047
##  ARIMA(1,0,2)            with non-zero mean : 16042.81
##  ARIMA(0,0,2)            with non-zero mean : 16062.67
##  ARIMA(2,0,0)            with non-zero mean : 16058.05
##  ARIMA(2,0,2)            with non-zero mean : 16044.74
##  ARIMA(1,0,1)            with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)            with non-zero mean : 16040.25
## 
##  Best model: ARIMA(1,0,1)            with non-zero mean
## Series: tsdata7 
## ARIMA(1,0,1) with non-zero mean 
## 
## Coefficients:
##          ar1      ma1       mean
##       0.9369  -0.8483  41260.458
## s.e.  0.0341   0.0532   3178.222
## 
## sigma^2 estimated as 1.215e+09:  log likelihood=-8016.12
## AIC=16040.25   AICc=16040.31   BIC=16058.31
library(tseries)
adf.test(mymodel7$residuals)
## Warning in adf.test(mymodel7$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel7$residuals
## Dickey-Fuller = -8.2756, Lag order = 8, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel7$residuals)

acf(ts(mymodel7$residuals),main = "ACF Residual")

pacf(ts(mymodel7$residuals),main = "PACF Residual")

myforecast7 <- forecast(mymodel7, level =c (95), h= 3*12)
plot(myforecast7,main = "Salary forecasting for American Indian in next 3years",xlab = "Time", ylab ="Average salary")

myforecast7[["mean"]]
##         Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 57                            34510.79 34936.44 35335.26 35708.92 36059.02
## 58 37504.89 37741.73 37963.63 38171.54 38366.34 38548.85 38719.85 38880.07
## 59 39541.76 39650.15 39751.70 39846.85 39936.00 40019.52 40097.78 40171.10
## 60 40473.92 40523.52 40569.99                                             
##         Sep      Oct      Nov      Dec
## 57 36387.04 36694.37 36982.32 37252.11
## 58 39030.19 39170.84 39302.61 39426.08
## 59 40239.80 40304.16 40364.47 40420.98
## 60

forecasting for race 4: Alaska Native

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
Anrace <- subset(newrace, race_name == "Alaska Native",select = c(avg_wage, year,race_name))
head(Anrace)
Anrace$avg_wage <- as.numeric(Anrace$avg_wage)
Anrace$Date <- paste (Anrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata8 <- ts(Anrace$avg_wage,frequency = 12)
tsdata8
##         Jan       Feb       Mar       Apr       May       Jun       Jul
## 1 122884.00  18151.70  12862.30 126053.00  60024.20  55463.40  21504.80
## 2 172570.00 126198.00  54068.30 172768.00  13016.40  20857.00  45056.90
## 3  10012.60  12877.10  18242.60  21529.50   1001.26 123026.00  37674.50
## 4  30227.60  56228.80  23513.90  18402.20  25954.20  10139.20  21801.60
##         Aug       Sep       Oct       Nov       Dec
## 1  25600.90  18221.60  20833.10  23193.80  21437.20
## 2  55527.20  23220.50  18172.50  25630.40  21461.90
## 3 127793.00  54751.50  13180.90  21120.60  45626.20
## 4   1013.92 124580.00  23015.90
ddata8 <- decompose(tsdata8, "multiplicative")
plot(ddata8)

plot(ddata8$seasonal)

plot(ddata8$trend)

plot(Anrace$avg_wage,type ="l",main = "Average salary for Alaska Native",xlab= "Year",ylab="Average Salary")

class(tsdata8)
## [1] "ts"
mymodel8 <- auto.arima(tsdata8)
mymodel8
## Series: tsdata8 
## ARIMA(0,0,0) with non-zero mean 
## 
## Coefficients:
##            mean
##       45575.874
## s.e.   6650.744
## 
## sigma^2 estimated as 2.08e+09:  log likelihood=-558.24
## AIC=1120.49   AICc=1120.77   BIC=1124.15
auto.arima(tsdata8,ic="aic",trace=TRUE)
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : Inf
##  ARIMA(0,0,0)            with non-zero mean : 1120.489
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 1123.793
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 1123.786
##  ARIMA(0,0,0)            with zero mean     : 1150.851
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 1122.156
##  ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 1122.096
##  ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 1123.91
##  ARIMA(1,0,0)            with non-zero mean : 1122.274
##  ARIMA(0,0,1)            with non-zero mean : 1122.311
##  ARIMA(1,0,1)            with non-zero mean : 1123.996
## 
##  Best model: ARIMA(0,0,0)            with non-zero mean
## Series: tsdata8 
## ARIMA(0,0,0) with non-zero mean 
## 
## Coefficients:
##            mean
##       45575.874
## s.e.   6650.744
## 
## sigma^2 estimated as 2.08e+09:  log likelihood=-558.24
## AIC=1120.49   AICc=1120.77   BIC=1124.15
library(tseries)
adf.test(mymodel8$residuals)
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel8$residuals
## Dickey-Fuller = -3.3059, Lag order = 3, p-value = 0.08263
## alternative hypothesis: stationary
plot.ts(mymodel8$residuals)

acf(ts(mymodel8$residuals),main = "ACF Residual")

pacf(ts(mymodel8$residuals),main = "PACF Residual")

myforecast8 <- forecast(mymodel8, level =c (95), h= 3*12)
plot(myforecast8,main = "Salary forecasting for Alaska Native in next 3years",xlab = "Time", ylab ="Average salary")

myforecast8[["mean"]]
##        Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 4                                                                        
## 5 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87
## 6 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87
## 7 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87 45575.87
##        Sep      Oct      Nov      Dec
## 4                   45575.87 45575.87
## 5 45575.87 45575.87 45575.87 45575.87
## 6 45575.87 45575.87 45575.87 45575.87
## 7 45575.87 45575.87

forecasting for race 5: Other Native American

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
onrace <- subset(newrace, race_name == "Other Native American",select = c(avg_wage, year,race_name))
head(onrace)
onrace$avg_wage <- as.numeric(onrace$avg_wage)
onrace$Date <- paste (onrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata9 <- ts(onrace$avg_wage,frequency = 12)
tsdata9
##           Jan        Feb        Mar        Apr        May        Jun
## 1   57379.200 164120.000  26796.500  51201.900  31619.900  15006.100
## 2   40469.700  17506.200  10718.600  37499.500   8192.300  36737.000
## 3   11187.700  10790.100  21036.800  37515.100  39388.900  93001.600
## 4  120355.000  10941.400  52082.700  20480.700  26215.900  45025.200
## 5   40809.200  25712.200   7717.400  57291.000  45018.200  28454.600
## 6   34413.700  20812.300  44600.500  78124.000  35992.400  41666.200
## 7   15624.800  10588.500  11790.500   1024.040  70905.600  15624.800
## 8   22174.100  20833.100  30721.100  20688.000   7680.280  27339.400
## 9   70658.600  66562.400  82060.200  20905.900   6413.120  52516.200
## 10  28228.700  47791.500  37830.600   6645.540  15541.800  37499.500
## 11  83605.200  63788.000  41879.300  11246.700  30012.100  42353.900
## 12  51260.800  29719.200  39240.000  70739.800  37542.700  56070.800
## 13  64656.800  36045.500  43059.000  10730.900  43278.400  23220.500
## 14  34339.000  48391.300  23509.100  16740.300  36499.800  51488.800
## 15  37558.300  18980.200  41714.100  31656.300  32192.800  36088.700
## 16  14762.500  11804.000  20522.100  18631.200  19320.400  10600.600
## 17  26827.300  47846.500  42402.600 141181.000  30287.600   3204.040
## 18  19507.800  27900.400  37558.300  15535.300  27342.300  30046.600
## 19  16192.500  52142.600  28658.900  31191.800  38234.000  30756.400
## 20    100.126 429542.000  78213.900   7726.280  53654.700  15642.800
## 21  21899.900  42857.900  33369.700   6653.180  45069.900   8201.720
## 22  10428.500  27908.700  10223.400  58081.600  20064.600  51908.500
## 23  17426.600  42241.200  27603.200  62290.200  50695.800  50024.500
## 24  10939.100  23174.500  51908.500   6448.560 125798.000  79109.900
## 25  36961.100 109045.000  67835.700  96728.400  42656.900  37448.600
## 26  18136.600  35515.200  18076.800  10736.300   7429.830   9043.930
## 27  10734.600  17129.000  38017.100   7786.270  42816.900   6458.930
## 28   3244.530  45626.200  17025.300  22823.400  24916.100  20000.700
## 29  62290.200  44641.300  23287.600  15840.500  15894.200  63923.700
## 30  20244.500 121670.000  10075.900  67481.000  32445.300  38017.100
## 31  50695.800  42938.400  20151.800  16628.300  22176.600  33855.000
## 32  49633.400  60835.000  33793.000  10560.300                      
##           Jul        Aug        Sep        Oct        Nov        Dec
## 1   23193.800  24727.500  15208.500  37889.400  30889.700  10302.800
## 2    8798.640  10084.200  82060.200  64582.500  61442.200  29817.600
## 3   10249.700  13129.600  33463.400  37305.500  36598.800   5173.600
## 4   53593.100  31323.200  11755.600  31817.800  51201.900  17023.900
## 5   61442.200  24576.900  20691.400  23202.400  21261.600  42671.300
## 6   30252.800  20030.400  28878.900  48957.700  10416.500  15809.700
## 7    1041.650  24713.800  31463.000  25724.700  43009.600  65648.200
## 8   41666.200  21874.700  42395.100  24055.900  37515.100  18221.600
## 9   27868.400  51429.700  19892.600 156039.000  27353.400  34299.600
## 10  25165.100  49286.100  25251.900  43228.600  17778.100  16077.900
## 11  10072.300  36457.900  51260.800  10095.800  57356.900  19814.200
## 12  43653.700  83701.300  41714.100  88920.700  61512.900  50063.200
## 13   1025.210  10802.600  51260.800 119561.000 121384.000  41595.600
## 14  68963.900  39279.000  34866.000  27699.400  28073.100  30287.600
## 15  30831.700  20504.300  11058.100  13521.900   9079.520  13060.900
## 16  37542.700   7689.110  30244.600  24725.900   9719.500  17109.000
## 17  15023.300  45056.900  16096.400  23309.100  18108.300  24605.200
## 18  27372.800  20504.300  61512.900  44675.700  27724.800  15642.800
## 19  35720.900  19865.100 120152.000  66639.000  32040.400  37542.700
## 20  37933.000  50063.200  42402.600  22511.700  18242.600  25579.900
## 21  22711.900  20857.000   6323.000  49014.000  60075.800  33371.300
## 22  24570.100  57432.500  46099.300  70751.700  38017.100  56779.300
## 23  36501.000  43603.100  88667.700  43825.200  23513.900   1038.170
## 24  34362.400  50637.200  25215.900   3022.760  64485.600  38288.300
## 25  29403.600  50728.700  30670.300  19220.000  42241.200  24810.300
## 26  15080.200  20278.300  13300.200  10188.200  20753.700  18365.800
## 27  10645.500  49951.100  44983.000 349546.000  70531.200  41532.400
## 28  22166.900  22368.500  16168.700  24495.700  26511.100  20763.400
## 29  28324.600  30499.800  19453.200  35165.800  31145.100  29347.300
## 30    101.392 434970.000  65254.900  24685.900  15840.500  38412.300
## 31  39404.800  30309.200   8305.360  19461.600  21120.600   1557.260
## 32
ddata9 <- decompose(tsdata9, "multiplicative")
plot(ddata9)

plot(ddata9$seasonal)

plot(ddata9$trend)

plot(onrace$avg_wage,type ="l",main = "Average salary for Other Native American",xlab= "Year",ylab="Average Salary")

class(tsdata9)
## [1] "ts"
mymodel9 <- auto.arima(tsdata9)
mymodel9
## Series: tsdata9 
## ARIMA(0,0,0) with non-zero mean 
## 
## Coefficients:
##           mean
##       37598.30
## s.e.   2125.52
## 
## sigma^2 estimated as 1.703e+09:  log likelihood=-4529.12
## AIC=9062.24   AICc=9062.27   BIC=9070.1
auto.arima(tsdata9,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 9074.159
##  ARIMA(0,0,0)            with non-zero mean : 9062.236
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 9066.661
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 9064.762
##  ARIMA(0,0,0)            with zero mean     : 9287.899
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 9065.127
##  ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 9064.234
##  ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 9067.009
##  ARIMA(1,0,0)            with non-zero mean : 9063.454
##  ARIMA(0,0,1)            with non-zero mean : 9062.763
##  ARIMA(1,0,1)            with non-zero mean : 9064.197
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(0,0,0)            with non-zero mean : 9062.236
## 
##  Best model: ARIMA(0,0,0)            with non-zero mean
## Series: tsdata9 
## ARIMA(0,0,0) with non-zero mean 
## 
## Coefficients:
##           mean
##       37598.30
## s.e.   2125.52
## 
## sigma^2 estimated as 1.703e+09:  log likelihood=-4529.12
## AIC=9062.24   AICc=9062.27   BIC=9070.1
library(tseries)
adf.test(mymodel9$residuals)
## Warning in adf.test(mymodel9$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel9$residuals
## Dickey-Fuller = -6.4511, Lag order = 7, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel9$residuals)

acf(ts(mymodel9$residuals),main = "ACF Residual")

pacf(ts(mymodel9$residuals),main = "PACF Residual")

myforecast9 <- forecast(mymodel9, level =c (95), h= 3*12)
plot(myforecast9,main = "Salary forecasting for Other Native American in next 3years",xlab = "Time", ylab ="Average salary")

myforecast9[["mean"]]
##        Jan     Feb     Mar     Apr     May     Jun     Jul     Aug     Sep
## 32                                 37598.3 37598.3 37598.3 37598.3 37598.3
## 33 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3
## 34 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3 37598.3
## 35 37598.3 37598.3 37598.3 37598.3                                        
##        Oct     Nov     Dec
## 32 37598.3 37598.3 37598.3
## 33 37598.3 37598.3 37598.3
## 34 37598.3 37598.3 37598.3
## 35

forecasting for race 6: Asian

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
asrace <- subset(newrace, race_name == "Asian",select = c(avg_wage, year,race_name))
head(asrace)
asrace$avg_wage <- as.numeric(asrace$avg_wage)
asrace$Date <- paste (asrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata10 <- ts(asrace$avg_wage,frequency = 12)
tsdata10
##            Jan        Feb        Mar        Apr        May        Jun
## 1    52914.100  26041.300  99637.800  19293.500  64820.600 161415.000
## 2    42411.200  82837.400  27054.900   8499.510  32155.800  32824.100
## 3    58952.400  14201.300  64195.500  27017.000  52518.500  13070.100
## 4    53355.000  50471.400  72095.700  65648.200  34605.000  42435.000
## 5    43135.900  21300.000  22439.500  32538.200  17997.600  28939.000
## 6    53821.100  24289.200  37111.100  49911.000  29046.100  22490.600
## 7    47276.800  80542.000  89303.100  28059.300  70750.400  51951.700
## 8    65648.200  20845.200  22100.100  75902.700  34957.100  20718.100
## 9     5999.350  30012.100  27099.000  22873.600  22981.200  85617.600
## 10   38228.500  35300.100  13231.400  37320.900   4006.750  48668.000
## 11   74366.900  24000.800  37269.800  32916.300  42254.600  35776.200
## 12   89440.600  37671.100  20975.200  25816.600  30323.300  11147.400
## 13   39304.400  58332.600 104452.000  46352.500  36118.200  27099.300
## 14   41930.500  20681.400  42736.000  60485.400  42638.400  49153.800
## 15   27353.400  55736.800  57565.000  69763.100  28143.000  22013.600
## 16   49211.100  27353.400  28217.200  23620.600  26796.500   9959.910
## 17   61708.100  36304.800  40438.600  78052.400  37320.600  34016.300
## 18   22796.700  18880.600  80199.700  31438.800  17377.000  24083.700
## 19   27127.700  23289.400  42206.900  35219.500  17706.100   6088.060
## 20  101827.000  59250.700  90910.900  29803.800  56751.400  73688.000
## 21   22647.900  26705.700  41101.000  37259.300  71179.000  16555.600
## 22   36874.600  75451.300  36155.800  31797.400  33186.300  18711.600
## 23    5042.120  26796.500  16100.200  95321.400  59361.400  11790.500
## 24   24845.500  42748.100  55750.100   6041.590  67121.000  71170.300
## 25   14270.100  73997.600  21261.200  16081.300  35200.800  25535.900
## 26   83531.300 145832.000  88119.000  16345.900  13184.300  11045.600
## 27    6249.920  57251.300  53498.900  44399.400  45211.200  51563.600
## 28   19293.500  80861.200  50629.800  59456.600  51069.600  75892.100
## 29   29388.300  14223.800  67603.000  21026.000  37719.600   8627.920
## 30   34266.700  47980.400  32672.500  31632.700  56657.300  16297.300
## 31   77822.800  53971.100  57024.300  26607.100  82060.200  19320.700
## 32   19203.800  26435.200  11716.700  51697.400  23817.000  50360.500
## 33   12862.300  26867.300  49903.200  48296.400  34028.600   9279.630
## 34   45180.300  31976.700  29285.700  36095.100  23451.000  33800.100
## 35   76613.800  59799.100 191568.000  44674.700 135571.000  98708.700
## 36   55800.900  53212.700  91476.000  67562.800  52624.900  52595.700
## 37   49603.200  81933.000  44537.000  28398.400  68896.900   8839.600
## 38   50801.700  45860.600  74174.200  82969.600  62892.700 121172.000
## 39   77119.100  74099.800  38944.200  75446.200  86266.400  66154.300
## 40   95351.400  64575.500  53042.300  74038.100  71538.200  84112.000
## 41  145999.000  70744.500  49076.700  43051.300  46215.100  57155.300
## 42   28035.400  44930.100  41295.700  48883.300  88262.500  47385.700
## 43   33967.500   3836.280  20800.700  31091.300  83699.400  41118.000
## 44   13752.000  31809.200  20343.400  11856.300  14425.200  45296.500
## 45   25630.400  85063.100  30372.300  38925.600  30997.000   9043.090
## 46   88134.200 217811.000  76896.000  12463.400  62003.800  69379.500
## 47  104039.000  59020.200  41039.500  72879.400  49031.100  44995.200
## 48   53052.900  12302.600  26363.900  54068.300  31234.000  34875.000
## 49   27539.900  58399.700 100471.000 104572.000  21250.100  91742.000
## 50   34327.600   2885.650  28504.900  55522.700  22310.200  15436.100
## 51    9962.740  15708.200  29449.700  31380.100  18304.600  18696.400
## 52   52941.900  16268.500  16461.300   9385.670  22996.400  19116.200
## 53   15870.500  39860.900  67375.200  18351.800  20054.400  22036.500
## 54   58215.600  46066.300  97275.700  24705.700    363.451  48810.900
## 55   43994.600  19315.700  21690.900  37031.200  24030.300  27246.100
## 56   17144.900  49411.400  11804.000  33966.700  22098.700  44344.800
## 57   42003.400  41046.800  45453.700  26483.200  18575.500  31774.400
## 58   25735.800  30741.800  31977.900  24857.100  25553.800  64993.000
## 59    2120.130  40715.800  36011.500  31083.800  25754.300  33879.500
## 60   18771.300  43987.500  30517.400  48000.100  32954.100  45353.100
## 61    4005.060  20299.300  42467.700  70088.500  39191.600  61512.900
## 62   56945.000  26924.500  24585.400  27276.500  55262.600  30915.100
## 63   48220.000  36804.800  30263.000  43674.200  30405.300  27969.100
## 64   55527.200  71765.000  26794.700  57840.300  31877.300  30046.600
## 65   26561.900  39898.600  19315.700  26827.300  32234.400  24004.000
## 66   21102.900  27013.700  23855.600  17740.300  11927.900  16751.700
## 67   26322.800  25995.000  36917.000 109308.000 117795.000  16500.200
## 68  101005.000  90141.100  69542.200 169836.000  37429.200 137284.000
## 69   45727.500  47252.700  81488.700  67663.300  54809.800  38660.400
## 70   48710.400  82902.600  49682.800  31022.200  65921.000  49681.900
## 71   55930.900  44920.700  74796.600 100253.000  84410.400 122831.000
## 72   78382.700  75496.500  41524.900  81210.900  87133.500  68695.800
## 73   98889.000  20151.800  67882.200  58571.600  74852.500  78398.400
## 74   61636.200 147844.000  90781.800  51547.000  43926.400  48577.400
## 75   47305.000  59493.100  28389.600  53721.400  54190.800  35782.000
## 76   36273.200  43838.900  34927.700  31132.100   2230.620  22894.300
## 77   33996.400  34241.100  45825.700  10501.800  34281.400  29783.000
## 78   19560.200  25878.300  42597.700  25954.200  84968.100  26848.600
## 79  166484.000  54148.900  68873.300  89357.100 219873.000  90397.300
## 80   47573.400  92895.800  63117.700  95427.300  63478.000  46459.300
## 81   35033.000  53779.400  10020.100  60727.400  42157.700  26620.900
## 82   45670.600  20128.200  22199.000  59137.700 101741.000 105893.000
## 83   40552.800  29918.500   4665.110  32616.000   3003.820  28317.800
## 84   18705.300  15264.800  15977.500   8611.420  12686.500  28943.800
## 85   42915.700  21636.600  44039.600  16474.100  14587.300  12706.600
## 86   22974.700   5330.330  17222.000  42018.300  68991.300  18017.600
## 87   35621.200  62776.900  50832.300  43069.900  96552.300  21440.900
## 88   30586.500  33828.000  45077.100  38288.300  24434.600  35996.400
## 89   11998.500  58030.600  19960.900  55341.400  34856.700  22250.100
## 90   57724.100  57117.600  37457.100  50023.800  22236.400  20431.800
## 91   17578.700  25907.100  23283.700  32279.500  30227.600  23513.900
## 92   28313.000  24286.800   2146.920  40734.600  30844.900  29785.100
## 93   19008.500  95609.100  32854.400  48279.500  60455.300  33370.500
## 94   53263.000   4055.660  20555.800  38288.300  48694.700  70974.100
## 95   38951.200  35156.700  58354.100  27384.000  25454.000  27215.400
## 96   26302.500  45604.500  37939.600  20151.800  30519.500  44226.000
## 97   20487.600  20028.100   6045.530   9684.370  56228.800  72671.900
## 98   34947.300  27844.300  34166.600  25529.200  36183.800  33252.900
## 99   70464.900   8616.810  20276.400  26017.200  31196.200  18973.600
## 100  19874.600  22421.300  30078.300  26727.300  37383.500 112964.000
##            Jul        Aug        Sep        Oct        Nov        Dec
## 1    29192.100  56351.900 136108.000  86738.500  55879.600  37274.900
## 2    58794.700  89133.700 104414.000  43154.800  25311.600  41939.400
## 3    32678.400  24490.300  10077.100  27618.800  36196.700  31970.700
## 4    49236.100  45147.400  47161.900  17779.400  57543.200  30063.600
## 5    67707.500  52598.900 125508.000   8950.310  63540.900  73078.400
## 6    35416.200  97593.500  46839.500  57480.800  72886.500  95665.500
## 7    75588.800  37012.200  55255.400  22326.000  30691.500  68379.900
## 8     6997.290  33008.700  26968.900  36457.900  29333.800  51648.900
## 9    27079.000  42694.800 137452.000  48982.400  82975.200  48676.000
## 10   28275.300  14967.000 109414.000  35520.800  60328.100  81502.200
## 11   48484.600  30721.100  38893.000  14661.400  39697.000  46203.400
## 12   24125.000  83492.500 124233.000  80635.600  94479.700 110170.000
## 13   34523.600  48248.700  68280.000  56845.600  41613.500 100356.000
## 14   21998.900  16400.200  27078.000  19101.200  46193.300  12288.400
## 15   22101.000  24071.000  47018.200  24868.300  22276.300  33719.200
## 16   11189.100  25993.000 104251.000  50648.500  40699.300  71540.100
## 17   66900.900  65515.700  41159.100  16554.000  34292.900  32597.900
## 18   35256.800   7398.770  61258.200  38421.000  53640.400  48129.700
## 19   49085.600  75341.800  73615.000  57994.800  78569.900  22052.200
## 20   48887.300  32863.600    914.926  15634.300 109353.000  57596.000
## 21   14637.900  50965.300  40121.200  36212.100  21261.400  85857.400
## 22   31911.800  25987.000  48940.800  20275.900   9409.570  30197.800
## 23   59214.100   4299.230 105174.000  74149.900  26153.900  63804.600
## 24   16128.400  47690.600  61971.100  21378.000  58967.200  40097.000
## 25   32151.400  39167.800  12441.800  45609.300  17261.100  24697.200
## 26   51103.700  48937.000  70598.700  74674.300 214449.000  36790.000
## 27   71682.600  39629.000  16848.100  21759.300  25962.100  71385.800
## 28   43010.400 194698.000  56162.000  40045.600  48736.300  20898.300
## 29   56363.300  22768.000  26086.800  25724.700   2457.690 212932.000
## 30   29747.200  20910.700  27857.700  30176.600  50494.200  53385.400
## 31   15751.600  16412.000  36012.400  46298.400  24447.000  31660.000
## 32   41483.500  17866.500  43624.000  33223.900  75417.400  55812.500
## 33   23228.900  16249.800  58626.100  32155.800  18299.100  57195.800
## 34   78501.900 124900.000 176622.000  84397.400  64513.500  96968.500
## 35   72242.600   9623.260  80493.200  66845.200 247168.000  46847.400
## 36   26190.300  86052.500  63614.000  37911.600  42832.000  55005.900
## 37   13854.800  41818.700  62534.800  56028.400 103335.000  51610.700
## 38   49775.800  33083.500  20216.300  39702.200  72970.400  75944.800
## 39   95926.100  53408.800  76704.400 100666.000  68817.200  69607.400
## 40   90704.100  74619.800  70577.500 105496.000  88302.200  57889.400
## 41   36149.300  71232.400  48492.700  49486.900  31789.600  54339.300
## 42   70068.400  19792.000  29128.300  57702.200  51097.500  40621.500
## 43   41351.600  55433.600  16393.600  35199.800  30553.700  40903.400
## 44   51501.600  23760.700  78444.200  21780.700  23888.800  47282.800
## 45   60575.100  45066.100  63614.000 142046.000  53300.900  60205.900
## 46   55959.700  51220.200  55244.700  50233.400 124760.000  65007.300
## 47   47952.800  36410.300  23193.800  33978.600  55653.900   6048.540
## 48   26719.300  30044.000  48289.200  21060.000  32192.800  13841.500
## 49   34274.500  83784.200  60589.500  40048.200  29292.500   9226.930
## 50   27403.300  22065.700   7989.510  18559.800  14546.100  17475.600
## 51   52065.700  19281.300  31346.400  50077.900  32192.800   4292.380
## 52   15470.300  31761.200   9530.590  27411.100  25730.400   8285.180
## 53   29058.300  43123.100  69966.900  34522.100  39906.200  56234.700
## 54   53576.600  48778.900  35457.000  34307.500  30870.800  34587.500
## 55   25800.000  28090.700  39736.100  21807.700  11719.500  45388.000
## 56   39784.300  23803.200  21324.500  27546.200  27271.800  56198.600
## 57   31480.900  31813.000  26107.500  19600.700  40489.200  17070.300
## 58   48185.100  30394.700  20306.700  34086.500  28660.300  25915.600
## 59   16560.700  46010.900  11160.200  26827.300  20999.400  22027.800
## 60   16853.200  44304.300  36023.800  30777.300  46217.300  49267.700
## 61   62325.400  40414.200  30147.300  30931.500  38465.200  35595.200
## 62   49210.300  22890.400  26850.500  47760.600  28973.500  26072.000
## 63   28734.500  18193.400  26508.100  21753.000  20349.900   9388.050
## 64   19201.600  32806.900  36579.500  27059.500  33961.500  25463.900
## 65   30886.200  38886.600  81154.800  37673.100  69510.500   8509.280
## 66   26837.400  12877.100  36499.800  36348.100  21445.700  23210.100
## 67   62552.000  77908.700 123282.000 349546.000  74184.700  56691.800
## 68  112806.000  74127.800   9393.550  67689.000  61300.000 218212.000
## 69   26450.100  84341.800  64417.800  37450.600  47152.800  60146.700
## 70   14187.100  40556.600  86239.700  52490.300 101510.000  54282.300
## 71   78418.000  30346.500  28386.900  54932.500 121525.000  76768.700
## 72   97215.100  54638.900  79139.700  72648.600  74037.800  73963.900
## 73   86810.300  82948.000  73995.700  87416.200 106866.000  83817.100
## 74   47962.600  32299.200  64760.200 191442.000  49105.400  57923.900
## 75   89142.700  50013.600  70721.600  39291.700  31214.500  55486.300
## 76   41290.800  86522.300  42739.500  41098.200  54244.000  18513.900
## 77   17029.200  14706.600  35736.100  41446.600  24179.800  83692.400
## 78   47185.200  33411.000   8877.540  61340.500  45371.700  64417.800
## 79   22961.400  64974.500  78447.800  56296.600  54982.900  55934.100
## 80   70158.900  52781.900  47867.100  49755.900  37839.200  23329.000
## 81   54751.500  31076.300  35060.800  26109.400  29374.100  16598.100
## 82   25110.900  96888.400  34282.200  84526.200  64015.600  30227.600
## 83   49728.500  23159.000  15255.800  24466.700  21259.900   8113.750
## 84   31566.800  20299.400  19640.000  52723.600  25145.300  31960.900
## 85   23212.700  20154.000  19665.000  27325.900   9200.380  18175.900
## 86   24093.100  19785.400  34452.400  38390.100  93115.800  30142.200
## 87     368.043  48352.100  53613.400  29790.500  35905.000  35911.200
## 88   24334.000  23668.700  22665.200  29661.100  35413.600  23384.700
## 89   45789.300  42938.600  24160.600  21593.900  32549.200  14912.700
## 90   34203.600  30843.600  38955.300  28676.500  21474.400  39642.900
## 91   29482.700  76655.600  48794.000  33983.900  21956.600  36884.900
## 92   34635.400  16849.600  49062.100  35668.600  21264.700  29297.600
## 93   41526.800  21961.300  46495.600  32036.300  30572.300  49915.400
## 94   37737.900  62290.200  86272.900  30567.500  31650.100  26400.800
## 95   41395.000  31024.400  49832.200  23550.800  28351.200  47756.700
## 96   33206.700  28477.200  31045.200  18379.400  24955.400  21221.800
## 97   33587.100  58475.800  25889.800  19396.800  33221.400  54409.800
## 98   32759.600  24619.200  30081.600  37960.200  92000.700  38546.500
## 99   15610.100  13891.500  20648.500  35265.600  36961.100  38776.300
## 100  18097.200  63744.000
ddata10 <- decompose(tsdata10, "multiplicative")
plot(ddata10)

plot(ddata10$seasonal)

plot(ddata10$trend)

plot(asrace$avg_wage,type ="l",main = "Average salary for Asian",xlab= "Year",ylab="Average Salary")

class(tsdata10)
## [1] "ts"
mymodel10 <- auto.arima(tsdata10)
mymodel10
## Series: tsdata10 
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1     sar1       mean
##       0.9390  -0.7889  -0.0206  45460.386
## s.e.  0.0176   0.0311   0.0300   2793.969
## 
## sigma^2 estimated as 829756492:  log likelihood=-13976.16
## AIC=27962.32   AICc=27962.37   BIC=27987.75
auto.arima(tsdata10,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 27948.98
##  ARIMA(0,0,0)            with non-zero mean : 28159.26
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 28034.35
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 28077.88
##  ARIMA(0,0,0)            with zero mean     : 29508.48
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 27967.68
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 27946.98
##  ARIMA(2,0,2)            with non-zero mean : 27966.12
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 27944.97
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 27946.6
##  ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 27945.57
##  ARIMA(2,0,1)(2,0,0)[12] with non-zero mean : 27945.28
##  ARIMA(3,0,2)(2,0,0)[12] with non-zero mean : 27946.56
##  ARIMA(2,0,3)(2,0,0)[12] with non-zero mean : 27945.11
##  ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 27944.51
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27942.35
##  ARIMA(1,0,1)            with non-zero mean : 27961.03
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 27943.94
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 27962.55
##  ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 27945.85
##  ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 28057.75
##  ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 27948.09
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 27942.97
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 28126.27
##  ARIMA(0,0,2)(1,0,0)[12] with non-zero mean : 28030.17
##  ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 28002.83
##  ARIMA(1,0,1)(1,0,0)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27962.32
## 
##  Best model: ARIMA(1,0,1)(1,0,0)[12] with non-zero mean
## Series: tsdata10 
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1     sar1       mean
##       0.9390  -0.7889  -0.0206  45460.386
## s.e.  0.0176   0.0311   0.0300   2793.969
## 
## sigma^2 estimated as 829756492:  log likelihood=-13976.16
## AIC=27962.32   AICc=27962.37   BIC=27987.75
library(tseries)
adf.test(mymodel10$residuals)
## Warning in adf.test(mymodel10$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel10$residuals
## Dickey-Fuller = -10.957, Lag order = 10, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel10$residuals)

acf(ts(mymodel10$residuals),main = "ACF Residual")

pacf(ts(mymodel10$residuals),main = "PACF Residual")

myforecast10 <- forecast(mymodel10, level =c (95), h= 3*12)
plot(myforecast10,main = "Salary forecasting for Asian in next 3years",xlab = "Time", ylab ="Average salary")

myforecast10[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 100                                                               
## 101 45959.80 45909.02 45752.85 45823.38 45605.24 44049.38 46005.14
## 102 45437.06 45438.91 45442.87 45442.12 45447.27 45479.94 45440.23
## 103 45454.74 45455.08 45455.35 45455.69 45455.89 45455.51 45456.60
##          Aug      Sep      Oct      Nov      Dec
## 100          45935.92 45636.94 45604.05 45568.57
## 101 45065.84 45433.83 45441.01 45442.65 45444.28
## 102 45460.12 45453.05 45453.39 45453.80 45454.19
## 103 45456.45

forecasting for race 7: Native Hawaiian or Other Pacific Islander

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
nhrace <- subset(newrace, race_name == "Native Hawaiian or Other Pacific Islander",select = c(avg_wage, year,race_name))
head(nhrace)
nhrace$avg_wage <- as.numeric(nhrace$avg_wage)
nhrace$Date <- paste (nhrace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata11 <- ts(nhrace$avg_wage,frequency = 12)
tsdata11
##           Jan        Feb        Mar        Apr        May        Jun
## 1   73957.400  81923.000 100842.000  76589.500 131296.000  69671.000
## 2   64311.700  27353.400  52518.500  18073.900  34373.800   7373.070
## 3   85716.100  50600.600  82533.300  88540.600  16068.300   7291.580
## 4   27353.400  20243.600 115978.000  12316.000  32291.300  44471.500
## 5   28065.900  14999.800  21437.200  29541.700  74774.700  33135.900
## 6    9075.830  20686.400  65529.800 344787.000  18241.100  46188.000
## 7   18739.900  22686.900 361065.000  21938.300  67355.800  32155.800
## 8    2178.950  28124.700 129635.000  57346.100  61177.800  55093.200
## 9   17408.600  56765.700   7038.150  26827.300  25600.900  29547.200
## 10  60418.200  29244.300  15696.300  47174.000  26470.100  12807.000
## 11  82017.200  41714.100  75226.400  88642.400  29427.800  71894.000
## 12  20025.300  60487.700  25630.400  64992.000  69751.100  78213.900
## 13  32192.800  50161.200   6257.900  50063.200 130404.000  15019.000
## 14  20351.200  32040.400  34343.400  34339.000  63592.800  26839.800
## 15    820.172  32328.400  24147.300  16394.300  28157.000  25031.600
## 16  19897.300  21063.800  10637.100   7139.220 100958.000  22856.900
## 17  36499.800  29271.200    807.668  19024.000  31414.000   7299.960
## 18  29641.400  65082.200  82628.200  45056.900    512.607  57412.000
## 19  16020.200  29378.900  15017.100  21461.900  32119.800  74359.600
## 20  24230.000  98939.300  43960.800  24998.200  39628.400  12821.700
## 21  30227.600  83053.600  60455.300  42241.200  76650.100  89762.500
## 22  75702.400  30417.500  86899.100  20278.300  61252.000  90682.900
## 23  63361.800  22003.700  40041.100  50608.200  25794.300  50795.000
## 24  66105.300  26635.500  50094.800  41526.800  30227.600  21902.500
## 25  51908.500  37583.000  13929.800  12091.100  14160.500   3376.140
## 26  13728.400   4562.620  35496.000  12796.400   1022.340  17648.900
## 27  44574.100  35487.100  28716.300  40893.700  40129.100  36961.100
## 28  52387.300  17340.600  11027.600  30670.300  30628.400   8618.290
## 29  58137.500  60455.300  45085.300  51317.400  60311.900  60455.300
## 30  84111.300  19656.200  40129.100   9725.000  29647.900   5725.120
## 31  12983.700  10381.700   6906.460  10900.800  73922.100           
##           Jul        Aug        Sep        Oct        Nov        Dec
## 1   32155.800  34299.600  10798.100  40337.000  39582.900  40730.700
## 2   78124.000  89749.800  10240.400  99108.500   8682.070  22610.600
## 3   28348.700  50103.600   7291.580  25152.100  40699.500  25210.600
## 4   27721.100  36063.400  39105.400  22294.700  29170.300  61442.200
## 5   41666.200  19498.700  17173.300  25005.600   5647.180  44289.600
## 6   32701.300  13541.500    819.230  17322.400  10941.400  70232.800
## 7   98825.600    512.018  31387.600  10624.900  35371.400  54706.800
## 8   84842.700  18151.700  72213.000  40337.000  43967.300  24017.600
## 9   14336.500  27093.200  16412.000  18432.700  29345.200  40483.000
## 10  39582.900  10173.500  30252.800  31133.600  97342.700   7381.550
## 11  89853.100  47059.400 345184.000  74727.200  30037.900  85814.700
## 12   7299.960  31635.600  77483.900  21602.100  40233.800  47127.800
## 13  29278.000  56136.600  52796.200  25239.600  49469.700  41008.600
## 14   9086.260  36814.200  13354.500  22320.400   8692.060  12115.000
## 15  13557.100   4505.690  23579.900   1009.590  17428.700  26902.800
## 16  44017.900  35044.200  40383.400  39628.400  40777.600  35412.100
## 17  49868.600  16965.400  15066.500  30287.600  31721.400   8510.740
## 18  64385.600  44522.700  50725.700  40383.400  14353.000  32192.800
## 19  18994.200  32451.900   9902.510  29278.000   5653.680  25630.400
## 20  10252.200  21911.400  12542.000  72999.600  99868.500  75596.700
## 21  28704.200  96728.400  48276.400  90988.400  47654.100 313544.000
## 22  25954.200  64759.000  79202.200  29220.100  11460.200  28861.100
## 23   6675.620 429232.000  64880.900 128180.000  15208.700  29647.900
## 24  32445.300  31176.500  40303.500  42241.200  20470.400   9201.080
## 25  32736.900  22349.400  20283.400  28512.800  25347.900   3325.040
## 26  24282.300  19997.800  15859.600  10771.500   6722.280 102234.000
## 27  24777.100    817.874  20730.900  28212.500  23851.900   6681.930
## 28  35102.100   3526.560  15845.500  35265.600  45626.200  10048.900
## 29  14534.400  40303.500  16222.700  29750.200  15206.800  34551.100
## 30  25954.200  24536.200 100190.000  41928.900  26664.600  40129.100
## 31
ddata11 <- decompose(tsdata11, "multiplicative")
plot(ddata11)

plot(ddata11$seasonal)

plot(ddata11$trend)

plot(nhrace$avg_wage,type ="l",main = "Average salary for Native Hawaiian or Other Pacific Islander",xlab= "Year",ylab="Average Salary")

class(tsdata11)
## [1] "ts"
mymodel11 <- auto.arima(tsdata11)
mymodel11
## Series: tsdata11 
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1    sma1       mean
##       0.8960  -0.8289  0.0826  41985.410
## s.e.  0.0869   0.1083  0.0554   4162.487
## 
## sigma^2 estimated as 2.062e+09:  log likelihood=-4430.02
## AIC=8870.03   AICc=8870.2   BIC=8889.53
auto.arima(tsdata11,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 8879.224
##  ARIMA(0,0,0)            with non-zero mean : 8876.082
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 8880.256
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 8875.274
##  ARIMA(0,0,0)            with zero mean     : 9093.531
##  ARIMA(0,0,1)            with non-zero mean : 8877.038
##  ARIMA(0,0,1)(1,0,1)[12] with non-zero mean : 8880.604
##  ARIMA(0,0,1)(0,0,2)[12] with non-zero mean : 8877.1
##  ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 8879.49
##  ARIMA(0,0,1)(1,0,2)[12] with non-zero mean : 8882.554
##  ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 8873.851
##  ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 8878.79
##  ARIMA(0,0,0)(0,0,2)[12] with non-zero mean : 8875.698
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 8877.761
##  ARIMA(0,0,0)(1,0,2)[12] with non-zero mean : 8880.755
##  ARIMA(1,0,0)(0,0,1)[12] with non-zero mean : 8875.667
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 8868.253
##  ARIMA(1,0,1)            with non-zero mean : 8868.637
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 8877.001
##  ARIMA(1,0,1)(0,0,2)[12] with non-zero mean : 8869.9
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 8876.403
##  ARIMA(1,0,1)(1,0,2)[12] with non-zero mean : 8878.893
##  ARIMA(2,0,1)(0,0,1)[12] with non-zero mean : 8868.962
##  ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 8868.986
##  ARIMA(0,0,2)(0,0,1)[12] with non-zero mean : 8870.921
##  ARIMA(2,0,0)(0,0,1)[12] with non-zero mean : 8870.792
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 8870.89
##  ARIMA(1,0,1)(0,0,1)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 8870.035
## 
##  Best model: ARIMA(1,0,1)(0,0,1)[12] with non-zero mean
## Series: tsdata11 
## ARIMA(1,0,1)(0,0,1)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1    sma1       mean
##       0.8960  -0.8289  0.0826  41985.410
## s.e.  0.0869   0.1083  0.0554   4162.487
## 
## sigma^2 estimated as 2.062e+09:  log likelihood=-4430.02
## AIC=8870.03   AICc=8870.2   BIC=8889.53
library(tseries)
adf.test(mymodel11$residuals)
## Warning in adf.test(mymodel11$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel11$residuals
## Dickey-Fuller = -6.8276, Lag order = 7, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel11$residuals)

acf(ts(mymodel11$residuals),main = "ACF Residual")

pacf(ts(mymodel11$residuals),main = "PACF Residual")

myforecast11 <- forecast(mymodel11, level =c (95), h= 3*12)
plot(myforecast11,main = "Salary forecasting for Native Hawaiian or Other Pacific Islander in next 3years",xlab = "Time", ylab ="Average salary")

myforecast11[["mean"]]
##         Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 31                                              35232.38 37607.71 37634.66
## 32 37635.37 38037.31 37758.27 38438.15 43638.12 40718.99 40850.64 40968.61
## 33 41398.08 41459.13 41513.84 41562.86 41606.79 41646.15 41681.42 41713.02
## 34 41828.07 41844.43 41859.08 41872.21 41883.98                           
##         Sep      Oct      Nov      Dec
## 31 44355.46 39731.67 38818.25 39996.52
## 32 41074.31 41169.02 41253.89 41329.94
## 33 41741.34 41766.71 41789.44 41809.82
## 34

forecasting for race 8: Two or more races

library(forecast)
library(readr)
library(zoo)
newrace <- drop_na(race)
trace <- subset(newrace, race_name == "Two or more races",select = c(avg_wage, year,race_name))
head(trace)
trace$avg_wage <- as.numeric(trace$avg_wage)
trace$Date <- paste (trace$year,"/01/01",sep ="")
#xts(tsal$Date, as.Date(tsal$Date, format='%y%m%d'))
tsdata12 <- ts(trace$avg_wage,frequency = 12)
tsdata12
##             Jan         Feb         Mar         Apr         May
## 1    49198.9000  51690.6000  42973.7000  44766.0000  95088.9000
## 2    18464.3000  50983.0000  74983.6000   1466.7400  34849.6000
## 3    25114.5000  41063.1000  20205.5000   8665.7400  22885.3000
## 4    31576.6000  69790.8000  39696.7000  28449.1000  54186.6000
## 5    96135.4000  11673.5000  20168.5000  82816.1000  46895.7000
## 6    44054.5000  43765.6000  77374.7000  52225.9000 132140.0000
## 7    35311.4000  22645.9000  49903.7000  41577.2000  26796.5000
## 8    22492.5000  69671.0000  57271.7000   3958.2900  36987.8000
## 9   121638.0000  35985.5000  76376.7000  42975.4000  29613.9000
## 10   55736.8000  49153.8000  31530.4000  58261.5000  26689.3000
## 11   37646.6000  85748.9000  47661.7000  12101.1000  33853.8000
## 12   17143.2000  84445.1000  10718.6000  25183.5000  52438.1000
## 13   20951.1000  36307.3000  26173.0000  45403.4000  69744.9000
## 14   38062.3000  19584.8000  15283.3000  28528.8000  17022.2000
## 15   54426.6000   8094.1800  21221.2000  20103.1000   6249.9200
## 16   26215.9000  10145.7000  15594.2000  40476.8000  38992.4000
## 17   43319.4000  25407.6000  33091.0000  43417.6000  42510.2000
## 18   20113.5000  20059.3000   5405.7700  38211.4000  35094.9000
## 19   15190.3000  24600.7000  32269.9000  68612.2000  35246.8000
## 20   24777.6000  29559.6000  33142.0000  40763.9000  20285.2000
## 21   10663.8000 161348.0000   8036.4000  24443.5000  31910.8000
## 22   30852.6000  17092.9000   9374.8900  21716.6000  21566.9000
## 23   60109.8000  57291.0000  14438.9000 135441.0000 158973.0000
## 24   66885.2000  15360.6000  54856.7000  45178.2000  32039.2000
## 25   37201.5000  15968.8000  32298.2000  41663.3000   6855.4100
## 26   19688.1000  35012.4000  31764.5000  66745.3000 118310.0000
## 27   64917.5000  49656.6000  38541.2000  21806.7000  19998.2000
## 28   57538.9000  53266.1000  24196.7000  56536.7000  84180.6000
## 29   44834.3000   2606.8500  53802.5000  19407.7000  36335.9000
## 30   25250.7000  19057.6000  39683.9000  48847.1000  22188.3000
## 31   46782.3000  38615.8000  25906.4000  18216.4000  21437.2000
## 32   16841.9000  26802.8000  29864.6000  17392.0000  27349.8000
## 33   36608.9000  35351.4000  50970.5000  80966.1000   3129.5000
## 34   25999.1000  33270.2000  59555.8000  83888.8000  45715.2000
## 35   40089.1000  53654.7000  36853.9000  45625.9000 121492.0000
## 36   50085.7000  54248.6000  33892.7000  63273.1000  16750.6000
## 37   85796.8000  30854.4000  13353.8000  69751.1000  39457.1000
## 38   35661.0000  41496.0000  30255.4000   7517.5500  15378.2000
## 39   76253.9000  88666.8000  49525.6000  55335.2000  53067.0000
## 40   33532.7000  73879.3000  41652.3000 107309.0000  79183.8000
## 41   50216.7000  41269.9000  77123.9000  49210.3000  57978.9000
## 42   16706.1000  35596.8000  33574.0000  11414.4000 143107.0000
## 43   23860.1000   1436.6800  16267.0000   4830.1600  17569.1000
## 44   14814.4000  74967.5000  57356.9000  28405.3000  32235.2000
## 45   92666.0000  29320.5000  48060.7000  85263.5000 261509.0000
## 46  137893.0000   6257.1100 153588.0000  30410.2000  49239.2000
## 47   21868.7000  25963.5000  19506.1000  19343.6000  26765.5000
## 48   86482.0000  60644.5000  35653.1000  57072.6000  56755.3000
## 49   21908.8000  15967.2000  13432.5000  23985.8000  12582.5000
## 50   54381.6000  13801.5000  19554.7000  22629.9000  22114.0000
## 51   12115.0000  11632.4000  18239.0000  18570.6000  25215.7000
## 52   73830.0000  10254.7000  29488.1000  35171.9000  18546.4000
## 53   48812.3000  19987.7000  17827.8000  26031.2000  37228.1000
## 54   25294.3000  63751.3000  32966.1000  30037.9000  21018.2000
## 55   19810.7000  44168.1000  23709.8000  52116.9000  24226.5000
## 56   16470.4000  11505.3000  32840.9000  42842.0000  22768.7000
## 57    9619.7900  30037.9000   7762.0400  20142.7000  12302.6000
## 58   36238.1000  30742.7000  26312.1000  53757.8000  26827.3000
## 59   25202.6000  55832.5000  82052.7000  10730.9000  27427.7000
## 60   34470.6000  20793.4000  28469.9000  25396.6000  61360.5000
## 61   35257.4000  34240.3000  25956.4000  56641.9000  28706.0000
## 62   16099.4000  27189.0000  20813.6000  25681.9000  19096.8000
## 63   12619.8000  11466.9000  15749.5000  18662.6000  16096.4000
## 64   33717.3000  42278.0000  31468.0000   8201.7200  17162.9000
## 65   23925.2000  37046.8000  52018.0000 112642.0000  31868.6000
## 66   55271.2000  16273.1000  16248.0000  74299.8000  10016.3000
## 67   26827.3000  24507.0000  72999.6000  64545.1000  24190.3000
## 68   75073.3000  61150.2000  56794.6000  56839.4000  53962.5000
## 69   40597.3000 100702.0000  22294.4000  60220.2000  50495.7000
## 70   76084.1000  54452.6000  44875.0000  36443.1000  83954.4000
## 71   32102.3000  70754.0000  61179.5000  85369.9000  45049.3000
## 72   66523.7000  64385.0000  90960.3000  65264.3000  77786.4000
## 73   91190.7000  78263.0000 149769.0000  73018.9000  41447.5000
## 74   34441.4000  73113.9000  92561.1000  50985.8000  45476.7000
## 75   29779.8000  29489.4000  31552.4000  38140.2000  29068.8000
## 76   43930.7000  47054.7000  20328.6000  38239.5000  42558.1000
## 77    8007.2100  29961.4000  56912.4000  30367.0000  16619.3000
## 78   26790.3000  19682.6000  25637.5000  41145.4000  47322.7000
## 79   61894.4000   8564.5000  54706.1000  70368.1000  48708.2000
## 80   37925.1000  23073.6000  30180.2000  39337.4000  55092.9000
## 81   29110.5000  15164.1000  12091.1000  28012.8000  27046.4000
## 82   59006.8000   6625.9600  87664.4000  31301.3000   4012.9100
## 83   13214.9000   7906.8900  16966.3000  17163.3000  10154.7000
## 84   22400.3000  20278.3000  40972.7000   6644.2900  18426.8000
## 85   24409.5000  13155.4000   9450.3700  13812.0000  24152.1000
## 86   19157.1000  43416.7000  36306.8000  99004.1000  42024.1000
## 87   39811.0000   9888.6600  14198.4000  19990.6000  23522.7000
## 88   19596.6000  42241.2000  24671.2000  15462.2000  25149.3000
## 89   50379.4000  23058.8000  33351.7000  52577.6000  42374.7000
## 90   35076.3000  21508.2000  26046.8000  35011.9000  26635.7000
## 91   22688.6000  12458.0000  25558.6000  19219.7000  35674.1000
## 92   53451.1000  20312.5000  22668.2000  25863.9000  21354.3000
## 93   72546.3000  43058.3000 163575.0000  31265.9000  44310.4000
## 94  108820.0000  33975.1000  33142.3000  30732.0000  33303.9000
## 95   33326.4000  39073.1000  29402.4000  80977.3000  35905.0000
## 96   22873.3000  52946.7000  24182.1000  38050.7000  32183.3000
## 97    7097.4100  30096.9000  14784.4000  37514.9000  39073.1000
## 98   42813.4000  62303.3000  40180.8000  14836.3000  40159.8000
## 99   29560.8000  33961.3000  27961.4000   8305.3600  72671.9000
## 100  16746.1000  21192.7000  18136.6000  14467.5000  22891.3000
##             Jun         Jul         Aug         Sep         Oct
## 1    20226.3000 118473.0000  48361.1000  72926.1000  20686.9000
## 2    38531.9000  32600.9000  91260.0000  17485.3000  47286.9000
## 3    25819.2000  29580.5000  30564.2000   2048.0700 202415.0000
## 4    27340.8000  21799.3000  23751.7000   9499.5100  32436.9000
## 5    19307.4000  44764.3000  33548.9000  79874.9000  10858.7000
## 6    84240.5000  27898.0000  59964.9000  42826.0000  73929.0000
## 7    16859.2000  43753.1000  49215.4000  36389.5000  29151.0000
## 8    21259.7000   8192.3000  16222.1000  22985.1000  46688.6000
## 9     7503.0300  12127.1000  24584.8000  17543.6000  45507.8000
## 10   76889.1000  37515.1000  28673.0000  15683.3000  26483.5000
## 11   46198.3000  46120.9000  17248.2000  31973.4000  43054.1000
## 12   63680.5000  60275.9000  65129.3000  39761.0000  64227.8000
## 13   33858.5000  36486.2000  63189.6000  54452.7000  99512.3000
## 14   47956.1000  25210.6000  10507.0000  38541.2000  99331.6000
## 15   29064.4000  13943.9000  32972.7000  35956.5000  17383.8000
## 16   50048.5000  56957.5000  79917.0000  51807.6000  22292.7000
## 17   21607.1000  20476.3000  72915.8000  26259.3000  18190.4000
## 18   41114.6000  35416.2000  50865.7000  18958.1000  36136.0000
## 19   72072.4000  24276.5000  62656.8000  74221.5000  30459.3000
## 20   66534.5000  40859.5000  22170.1000  27967.0000  17683.7000
## 21    5949.7100  28772.6000  23962.1000  63100.4000  24478.9000
## 22   14583.2000  27353.4000  35208.3000  41337.8000  70126.9000
## 23   22813.3000  18878.9000  20904.7000  48709.6000  30635.8000
## 24   22424.9000  36342.5000  15557.1000  19385.0000  24730.9000
## 25   14664.4000  44552.5000  28441.7000 107186.0000  88482.4000
## 26  196762.0000  26082.6000  20450.8000  56919.5000  43198.4000
## 27   12605.3000  19262.1000  72936.3000  30495.6000  51331.5000
## 28   43902.6000  71682.6000  61360.6000  20270.8000  20398.7000
## 29   39700.9000  53723.9000  43383.9000  39821.0000  34006.6000
## 30   16077.9000  48194.3000  48848.6000  34453.4000  41364.0000
## 31   35102.7000  21580.3000  93001.6000  10941.4000  30959.3000
## 32   45299.8000  23359.9000  17668.9000  50543.3000  33108.9000
## 33   35534.9000  16946.3000  43679.7000  80299.1000  17588.1000
## 34   66044.6000  62407.5000  47730.1000  69749.6000  58210.3000
## 35   68176.4000  55075.9000  84589.8000  41924.4000  99445.9000
## 36   16171.2000  51546.9000  45273.8000  57182.8000  49951.9000
## 37   40258.7000  55500.6000  48143.2000  20588.1000  69871.1000
## 38   43816.0000  72835.7000  24343.9000  67663.7000  78872.1000
## 39   72429.8000  51357.0000  94076.4000  90451.2000  83463.3000
## 40   46756.3000  40360.4000  21605.1000  30797.3000  71734.2000
## 41   23355.6000  14257.1000  33225.4000  35830.5000  38728.2000
## 42   39868.5000  30738.0000  48285.9000  19923.4000  38494.6000
## 43   66882.7000  34765.8000  30365.2000  44959.2000  28787.4000
## 44   24272.2000  32046.9000  24685.6000  26422.6000  33908.7000
## 45  105020.0000  79333.6000  53673.3000  47882.1000  37357.9000
## 46   37223.2000  43887.5000  30198.6000  38426.7000  56112.7000
## 47   24199.0000  12877.1000  29418.4000  22034.3000  62493.7000
## 48    6509.4300  79796.3000  24646.4000   3962.8400  41051.8000
## 49    7753.4000  16663.9000  16084.5000  10880.4000   9248.0300
## 50   20025.3000  43703.3000   6561.3800  18803.2000   8978.3100
## 51   13415.8000  10025.1000  14141.4000  23044.0000   3656.8400
## 52   46819.3000  42148.7000  95791.8000  44672.8000  58082.6000
## 53    9756.7600  26706.3000  24001.3000  26919.9000  30010.6000
## 54   41714.1000  24047.1000  14483.5000  22787.0000  10628.0000
## 55   27785.7000  51251.3000  42237.4000  58493.1000  63140.2000
## 56   26033.1000  33617.0000  24760.4000  19640.1000  15133.2000
## 57   25239.6000  18979.9000  41818.9000  21505.1000  23854.1000
## 58   18801.9000  26251.1000  22130.9000  21367.0000  42634.8000
## 59   55800.9000  49380.0000 161534.0000  24183.1000  44713.5000
## 60   37558.3000  45295.3000  43230.3000  30565.1000  38061.3000
## 61   42174.2000  38585.5000  30794.8000  79966.8000  35457.0000
## 62   23016.9000  52286.0000  40603.3000  43045.5000  19404.9000
## 63    7008.8500  27716.9000  14599.9000  37046.8000  38585.5000
## 64   42960.0000  72999.6000  30289.5000  17121.6000  39884.8000
## 65   20710.7000  28629.0000  30468.3000  26862.5000   8201.7200
## 66   69751.1000  22629.5000  18642.1000  18524.3000  23608.1000
## 67   45430.0000  79006.2000  89159.6000  48615.5000  63515.9000
## 68   28212.5000  41356.9000  42645.4000  89926.3000  63545.1000
## 69   57481.0000  34321.0000  63259.0000   6864.1900  18874.4000
## 70   29537.1000  31680.9000  72546.3000  42712.9000  41193.5000
## 71   57068.6000  31128.4000   2919.1300  15572.5000  44369.6000
## 72   90835.7000  48994.6000  60954.4000  53737.5000  81124.8000
## 73   87648.6000  90682.9000  45944.0000  77209.2000  47483.2000
## 74   85761.6000  39977.0000  81862.9000  47860.4000  22374.7000
## 75   25597.7000  19756.8000  35329.3000  45829.0000  27623.1000
## 76   28735.3000  21618.8000   1508.0300  16315.7000   2534.4700
## 77    9530.4500  18584.3000  71755.1000  68750.9000  35002.4000
## 78   62848.6000 126737.0000  31147.5000  48668.0000  83228.0000
## 79   21946.9000  57312.8000 139216.0000   7041.6700 157969.0000
## 80   34715.6000  50281.9000  22787.1000  49271.1000  26120.3000
## 81   65975.7000  80443.5000  85255.8000  83603.0000  55225.1000
## 82   26209.4000  31933.6000  28870.0000  25304.4000  15823.4000
## 83    8517.3200   9103.1500  40469.3000  51885.2000  14605.2000
## 84   23619.3000   2561.6900  13692.3000  12268.1000  12906.7000
## 85    3054.3900  23131.1000  42862.6000  71234.9000  10970.0000
## 86   60035.2000  47602.7000  54956.0000  52077.7000  16706.4000
## 87   30340.2000  42361.3000  20278.3000  25134.7000  66149.7000
## 88    4660.7300  35451.1000  32059.3000  18895.5000  43113.2000
## 89   45403.8000  71002.2000  42099.2000  21729.3000  14311.2000
## 90   22013.5000  15324.4000  31967.5000  44866.2000  36033.7000
## 91   24482.7000  24521.1000  15048.0000  21470.2000  30966.2000
## 92   52831.4000  23399.0000  27192.8000  60905.1000  81113.3000
## 93   31018.6000  41890.8000  34575.8000     50.6958  28829.6000
## 94   35155.7000  48405.7000  36961.1000  31444.0000  34672.6000
## 95   25329.8000  56329.3000  19090.6000  25338.5000  20820.9000
## 96   26637.6000  13570.1000  10938.5000  14222.4000  16448.0000
## 97   70333.6000  18535.4000  34108.3000  43003.7000  31248.9000
## 98   60455.3000  14100.8000  23922.3000  33390.6000  77259.2000
## 99   60733.5000  55969.6000  23430.3000  20508.0000  64417.8000
## 100  24816.7000  65168.7000  64258.2000  27798.7000  43111.0000
##             Nov         Dec
## 1     6553.8400  80820.6000
## 2    30673.4000  36847.1000
## 3    32997.5000  17761.7000
## 4    98307.6000  52021.1000
## 5    53593.1000  40974.0000
## 6    28130.1000  56501.5000
## 7     8710.2200  26558.1000
## 8    21959.9000  44114.6000
## 9    33643.7000  37676.5000
## 10   28437.2000  27633.5000
## 11    5208.2700  28447.5000
## 12    6144.2200  79752.2000
## 13   39716.1000  12288.4000
## 14    3470.3200  65136.5000
## 15   19164.9000  26939.8000
## 16   23005.0000  75025.8000
## 17   12961.8000 129546.0000
## 18   23580.9000  41055.6000
## 19   50817.7000  72118.7000
## 20   24054.0000  73623.1000
## 21   26100.2000  17783.1000
## 22   14619.6000  13315.4000
## 23   57047.0000  41961.1000
## 24   34443.6000  19293.5000
## 25   12862.3000  28379.9000
## 26   58638.9000  42693.2000
## 27   33775.1000  51930.3000
## 28   21962.7000  59690.7000
## 29   89470.3000  38601.4000
## 30   94359.8000  35359.8000
## 31    6437.3700  27138.4000
## 32   35389.6000  44394.6000
## 33   19059.3000  13340.6000
## 34   58687.6000  80558.9000
## 35   22239.8000  77639.0000
## 36   47279.2000  40565.4000
## 37   55936.9000  51357.7000
## 38   87895.7000  57961.7000
## 39  133726.0000  72177.3000
## 40   80482.0000  91252.7000
## 41   33476.1000  27682.2000
## 42   39817.1000  33117.2000
## 43   16060.6000  15639.1000
## 44   55442.3000  20191.7000
## 45   34279.3000  54320.0000
## 46   28773.1000  48676.5000
## 47   76274.8000  85708.7000
## 48   35641.7000  28147.5000
## 49    8282.8500  33688.6000
## 50    2624.9000  11786.6000
## 51    9543.4400  37770.3000
## 52   45689.4000  68104.1000
## 53   44529.4000  20025.3000
## 54   35116.2000  40144.5000
## 55   41228.7000  21781.8000
## 56   31434.1000  31448.4000
## 57   14199.7000  22062.9000
## 58   21461.9000  19275.7000
## 59   30265.2000  33824.6000
## 60   38751.1000  48249.8000
## 61   25096.7000  51550.8000
## 62    8381.9700  18998.1000
## 63   56140.7000  22502.4000
## 64   19315.7000  19408.1000
## 65   71765.0000  62953.1000
## 66   17635.2000  19391.7000
## 67   62989.7000  48422.1000
## 68   55095.8000  82160.2000
## 69   52201.8000  45995.3000
## 70   48517.4000  44047.3000
## 71   70150.1000  81603.8000
## 72   55102.1000  87654.3000
## 73   34315.8000  29216.8000
## 74   50379.4000  14231.2000
## 75  129258.0000  40930.4000
## 76   17179.3000  72293.1000
## 77   51853.3000  29461.8000
## 78  226021.0000 118948.0000
## 79   33337.0000  51010.9000
## 80   25054.6000  17405.7000
## 81   40009.6000  57600.8000
## 82   12636.9000  24703.6000
## 83   20602.9000  25372.7000
## 84   18512.9000  26726.4000
## 85   30435.2000  37178.6000
## 86   20748.7000  27768.6000
## 87   33938.0000  30417.5000
## 88   39734.1000  50219.5000
## 89   15335.1000  32661.5000
## 90   30417.5000  10189.9000
## 91   31258.1000  25305.5000
## 92   18741.1000  29729.2000
## 93   32454.1000  59389.8000
## 94   52256.7000  29068.8000
## 95   25580.2000  20763.6000
## 96   15948.5000  17476.3000
## 97    8305.3600  17379.8000
## 98  114709.0000  34482.6000
## 99   17968.9000  22345.6000
## 100
ddata12 <- decompose(tsdata12, "multiplicative")
plot(ddata12)

plot(ddata12$seasonal)

plot(ddata12$trend)

plot(trace$avg_wage,type ="l",main = "Average salary for Two or more races",xlab= "Year",ylab="Average Salary")

class(tsdata12)
## [1] "ts"
mymodel12 <- auto.arima(tsdata12)
mymodel12
## Series: tsdata12 
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1    sar1      mean
##       0.8923  -0.7433  0.0038  40023.31
## s.e.  0.0347   0.0524  0.0300   1797.61
## 
## sigma^2 estimated as 684363021:  log likelihood=-13884.05
## AIC=27778.1   AICc=27778.15   BIC=27803.54
auto.arima(tsdata12,ic="aic",trace=TRUE)
## 
##  Fitting models using approximations to speed things up...
## 
##  ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 27774.69
##  ARIMA(0,0,0)            with non-zero mean : 27896.84
##  ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 27818.73
##  ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 27846.13
##  ARIMA(0,0,0)            with zero mean     : 29256.79
##  ARIMA(2,0,2)(0,0,1)[12] with non-zero mean : 27783.29
##  ARIMA(2,0,2)(1,0,0)[12] with non-zero mean : 27772.87
##  ARIMA(2,0,2)            with non-zero mean : 27781.31
##  ARIMA(2,0,2)(2,0,0)[12] with non-zero mean : 27776.63
##  ARIMA(2,0,2)(2,0,1)[12] with non-zero mean : 27777.27
##  ARIMA(1,0,2)(1,0,0)[12] with non-zero mean : 27771.81
##  ARIMA(1,0,2)            with non-zero mean : 27777.67
##  ARIMA(1,0,2)(2,0,0)[12] with non-zero mean : 27772.27
##  ARIMA(1,0,2)(1,0,1)[12] with non-zero mean : 27773.79
##  ARIMA(1,0,2)(0,0,1)[12] with non-zero mean : 27779.66
##  ARIMA(1,0,2)(2,0,1)[12] with non-zero mean : 27772.75
##  ARIMA(0,0,2)(1,0,0)[12] with non-zero mean : 27806.11
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27770.84
##  ARIMA(1,0,1)            with non-zero mean : 27776.35
##  ARIMA(1,0,1)(2,0,0)[12] with non-zero mean : 27771.61
##  ARIMA(1,0,1)(1,0,1)[12] with non-zero mean : 27772.83
##  ARIMA(1,0,1)(0,0,1)[12] with non-zero mean : 27778.33
##  ARIMA(1,0,1)(2,0,1)[12] with non-zero mean : 27772.35
##  ARIMA(0,0,1)(1,0,0)[12] with non-zero mean : 27835.5
##  ARIMA(2,0,1)(1,0,0)[12] with non-zero mean : 27770.9
##  ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 27884.64
##  ARIMA(2,0,0)(1,0,0)[12] with non-zero mean : 27790.96
##  ARIMA(1,0,1)(1,0,0)[12] with zero mean     : Inf
## 
##  Now re-fitting the best model(s) without approximations...
## 
##  ARIMA(1,0,1)(1,0,0)[12] with non-zero mean : 27778.1
## 
##  Best model: ARIMA(1,0,1)(1,0,0)[12] with non-zero mean
## Series: tsdata12 
## ARIMA(1,0,1)(1,0,0)[12] with non-zero mean 
## 
## Coefficients:
##          ar1      ma1    sar1      mean
##       0.8923  -0.7433  0.0038  40023.31
## s.e.  0.0347   0.0524  0.0300   1797.61
## 
## sigma^2 estimated as 684363021:  log likelihood=-13884.05
## AIC=27778.1   AICc=27778.15   BIC=27803.54
library(tseries)
adf.test(mymodel12$residuals)
## Warning in adf.test(mymodel12$residuals): p-value smaller than printed p-
## value
## 
##  Augmented Dickey-Fuller Test
## 
## data:  mymodel12$residuals
## Dickey-Fuller = -10.529, Lag order = 10, p-value = 0.01
## alternative hypothesis: stationary
plot.ts(mymodel12$residuals)

acf(ts(mymodel12$residuals),main = "ACF Residual")

pacf(ts(mymodel12$residuals),main = "PACF Residual")

myforecast12 <- forecast(mymodel12, level =c (95), h= 3*12)
plot(myforecast12,main = "Salary forecasting for Two or more races in next 3years",xlab = "Time", ylab ="Average salary")

myforecast12[["mean"]]
##          Jan      Feb      Mar      Apr      May      Jun      Jul
## 100                                                               
## 101 39627.19 39677.18 39695.23 39707.76 39763.19 39791.51 39962.88
## 102 39943.30 39951.94 39959.55 39966.33 39972.55 39978.02 39983.45
## 103 40003.00 40005.18 40007.14 40008.88 40010.43 40011.82 40013.06
##          Aug      Sep      Oct      Nov      Dec
## 100                            39552.97 39611.19
## 101 39976.18 39853.24 39924.48 39922.93 39933.76
## 102 39987.76 39991.11 39994.77 39997.80 40000.55
## 103 40014.16 40015.14 40016.02

Bias Within Job Classes: Computer programmers

newsal <- drop_na(Sal)
bsal <- subset(newsal, soc_name == "Computer programmers",select = c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(bsal)
ggplot(data= bsal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salaryfor Computer Programmers")

ggplot(data= bsal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Computer Programmers")

Bias Within Job Classes: Mechanical engineers

newsal <- drop_na(Sal)
mesal <- subset(newsal, soc_name == "Mechanical engineers",select = c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(mesal)
ggplot(data= mesal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Mechanical engineers")

ggplot(data= mesal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Mechanical engineers")

#Bias Within Job Classes: Electrical & electronics engineers

newsal <- drop_na(Sal)
eesal <- subset(newsal, soc_name == "Electrical & electronics engineers",select =
          c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(eesal)
ggplot(data= eesal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Electrical & electronics engineers")

ggplot(data= eesal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Electrical & electronics engineers")

#Bias Within Job Classes: Civil engineers

newsal <- drop_na(Sal)
csal <- subset(newsal, soc_name == "Civil engineers",select =
          c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(csal)
ggplot(data= csal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Civil engineers")

ggplot(data= csal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Civil engineers")

Bias Within Job Classes: Aerospace engineers

newsal <- drop_na(Sal)
asal <- subset(newsal, soc_name == "Aerospace engineers",select =
          c(avg_wage_ft,year,sex_name,num_ppl,soc_name))
head(asal)
ggplot(data= asal, mapping= aes(x= factor(year), y= avg_wage_ft,fill= sex_name))+geom_bar(stat = "identity",position = "dodge2")+labs(x= "year",y= "Average salary",title = "Year vs Average Salary for Aerospace engineers")

ggplot(data= asal, mapping= aes(x= factor(year),y= num_ppl,fill= sex_name))+geom_bar(stat= "identity",position = "dodge2")+labs(x= "year",y= "No. of Employees",title = "Year vs No.of Employees for Aerospace engineers")

#predicted monthly salary by races

ggplot(data= predictedrace,aes(x= factor(Year),y=mean))+geom_bar(stat="identity",position = "dodge",aes(fill=Race))+labs(title = "Predicted mean by racial group",x="year",y="Average salary")

##predicted monthly salary by races

library(plotly)
## Warning: package 'plotly' was built under R version 3.5.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(gganimate)
## Warning: package 'gganimate' was built under R version 3.5.3
predg <- subset(predictedrace,sex== "male" | sex=="female",select=c(year1,gender_mean,sex))
p<- ggplot(data= predg,aes(x= year1,y=gender_mean))+geom_bar(stat="identity",position = "dodge",aes(fill=sex))+labs(title = "Predicted mean by gender",x="year",y="Average salary")+transition_states(sex,transition_length=2,state_length=1)+enter_fade()+exit_shrink()+ease_aes("sine-in-out")
p

unem <- subset(predictedrace,select=c(year2,Unemploy))
unem<- drop_na(unem)
p<- ggplot(data= unem,aes(x= year2,y=Unemploy))+geom_bar(stat="identity", fill="Navy blue")+labs(title = "Predicted Unemployment rate",x="year",y="Unemployment rate")
p